Factors influencing patients' opt-in intention of exchanging health information

Introduction

Health information exchange (HIE) exhibits tremendous benefits in improving the quality of healthcare and reducing healthcare costs. However, it also poses challenges related to data security, data privacy, patient engagement, etc.

Objective

This study aimed to explore the factors affecting patients' opt-in intention to HIE by using an empirical study based on the theory of planned behavior.

Methods

A Web-based survey was conducted involving 501 valid participants in China (69% validity rate).

Results

Information sensitivity and perceived HIE transparency affected the patients' opt-in intention to HIE through the mediation of perceived behavior control and trust in HIE. Information sensitivity negatively influenced perceived behavior control (-0.551, P < 0.001) and trust in HIE (-0.489, P < 0.001). Perceived transparency of HIE positively influenced perceived behavior control (0.396, P < 0.001) and trust in HIE (0.471, P < 0.001). Moreover, patients' opt-in intention to HIE can be positively affected by perceived HIE transparency (0.195, P < 0.001) and trust in HIE (0.294, P < 0.001). In addition, the moderating effect of health status was positive and significant between trust in HIE and opt-in intention to HIE but not between the perceived behavior control and opt-in intention to HIE.

Conclusion

This study contributes to the theory of planned behavior and enriches the literature on HIE efforts. HIE administrators should design personalized health services on the basis of these different health statuses to successfully achieve patients' opt-in intention to HIE.

Rigor, Reproducibility, and Transparency in Shared Research Resources: Follow-Up Survey and Recommendations for Improvements

Rigor, reproducibility, and transparency (RR&T) are essential components of all scientific pursuits. Shared research resources, also known as core facilities, are on the frontlines of ensuring robust RR&T practices. The Association of Biomolecular Resource Facilities Committee on Core Rigor and Reproducibility conducted a follow-up survey 4 years after the initial 2017 survey to determine if core facilities have seen a positive impact of new RR&T initiatives (including guidance from the National Institutes of Health, new scientific journal requirements on transparency and data provenance, and educational tools from professional organizations). While there were fewer participants in the most recent survey, the respondents' opinions on the role of core facilities and level of best practices adoption remained the same. Overall, the respondents agreed that procedures should be implemented by core facilities to ensure scientific RR&T. They also indicated that there is a strong correlation between institutions that emphasize RR&T and core customers using this expertise in grant applications and publications. The survey also assessed the impact of the COVID-19 pandemic on core operations and RR&T. The answers to these pandemic-related questions revealed that many of the strategies aimed at increasing efficiencies are also best practices related to RR&T, including the development of standard operating procedures, supply chain management, and cross training. Given the consistent and compelling awareness of the importance of RR&T expressed by core directors in 2017 and 2021 contrasted with the lack of apparent improvements over this time period, the authors recommend an adoption of RR&T statements by all core laboratories. Adhering to the RR&T guidelines will result in more efficient training, better compliance, and improved experimental approaches empowering cores to become "rigor champions."

Corruption and disasters in the built environment: a literature review

This paper presents the findings of a review of academic literature concerning the degree to which corruption worsens disasters triggered by natural hazards in the built environment. The research employed a 'systematic literature review' methodology to analyse leading academic databases, resulting in a detailed analysis of 59 peer-reviewed, published papers. It found that while much of the literature focuses on earthquakes (relating to building and infrastructure collapse), the quality of governance, and the drivers of corruption, there is presently very limited scholarship on the general scope, reach, and scale with respect to how disasters are worsened by corruption. It is notable that the Sendai Framework for Disaster Risk Reduction 2015-2030 and a number of other high-level reports fail to mention corruption. The paper argues that this serious gap in understanding and expressing how corruption increases vulnerability in the built environment within disaster studies perversely supports the furtherance of corruption in worsening disasters.

HARmonized Protocol Template to Enhance Reproducibility of Hypothesis Evaluating Real-World Evidence Studies on Treatment Effects: A Good Practices Report of a Joint ISPE/ISPOR Task Force

OBJECTIVES

Ambiguity in communication of key study parameters limits the utility of real-world evidence (RWE) studies in healthcare decision-making. Clear communication about data provenance, design, analysis, and implementation is needed. This would facilitate reproducibility, replication in independent data, and assessment of potential sources of bias.

METHODS

The International Society for Pharmacoepidemiology (ISPE) and ISPOR-The Professional Society for Health Economics and Outcomes Research (ISPOR) convened a joint task force, including representation from key international stakeholders, to create a harmonized protocol template for RWE studies that evaluate a treatment effect and are intended to inform decision-making. The template builds on existing efforts to improve transparency and incorporates recent insights regarding the level of detail needed to enable RWE study reproducibility. The over-arching principle was to reach for sufficient clarity regarding data, design, analysis, and implementation to achieve 3 main goals. One, to help investigators thoroughly consider, then document their choices and rationale for key study parameters that define the causal question (e.g., target estimand), two, to facilitate decision-making by enabling reviewers to readily assess potential for biases related to these choices, and three, to facilitate reproducibility.

STRATEGIES TO DISSEMINATE AND FACILITATE USE

Recognizing that the impact of this harmonized template relies on uptake, we have outlined a plan to introduce and pilot the template with key international stakeholders over the next 2 years.

CONCLUSION

The HARmonized Protocol Template to Enhance Reproducibility (HARPER) helps to create a shared understanding of intended scientific decisions through a common text, tabular and visual structure. The template provides a set of core recommendations for clear and reproducible RWE study protocols and is intended to be used as a backbone throughout the research process from developing a valid study protocol, to registration, through implementation and reporting on those implementation decisions.

Value-based evidence across health care sectors: a push for transparent real-world studies, data, and evidence dissemination

There is currently a heightened need for transparency in pharmaceutical sectors. The inclusion of real-world (RW) evidence, in addition to clinical trial evidence, in decision-making processes, was an important step forward toward a more inclusive established value proposition. This advance has introduced new transparency challenges. Increasing transparency is a critical step toward accelerating improvement in type, quality, and access to data, regardless of whether these originate from clinical trials or from RW studies. However, so far, advances in transparency have been relatively restricted to clinical trials, and there remains a lack of similar expectations or standards of transparency concerning the generation and reporting of RW data. This perspective paper aims to highlight the need for transparency concerning RW studies, data, and evidence across health care sectors, to identify areas for improvement, and provide concrete recommendations and practices for the future. Specific issues are discussed from different stakeholder perspectives, culminating in recommended actions, from individual stakeholder perspectives, for improved RW study, data, and evidence transparency. Furthermore, a list of potential guidelines for consideration by stakeholders is proposed. While recommendations from different stakeholder perspectives are made, true transparency in the processes involved in the generation, reporting, and use of RW evidence will require a concerted effort from all stakeholders across health care sectors.

Research funders should be more transparent: a plea for open applications

Transparency is increasingly becoming the new norm and modus operandi of the global research enterprise. In this mini-review, we summarize ongoing initiatives to increase transparency in science and funding in particular. Based on this, we make a plea for the next step in funders' compliance with the principles of Open Science, suggesting the adoption of open applications. Our proposed model includes a plea for the publication of all submitted grant applications; open sharing of review reports, argumentations for funding decisions and project evaluation reports; and the disclosure of reviewers' and decision committee members' identities. In line with previous calls for transparency and the available evidence about these measures' effectiveness, we argue that open applications could lead to more diverse collaboration, recognition of research ideas, fairer procedures for grant allocation, more research on funding practices and increased trust in the funding allocation process.

Piloting a Survey-Based Assessment of Transparency and Trustworthiness with Three Medical AI Tools

Artificial intelligence (AI) offers the potential to support healthcare delivery, but poorly trained or validated algorithms bear risks of harm. Ethical guidelines stated transparency about model development and validation as a requirement for trustworthy AI. Abundant guidance exists to provide transparency through reporting, but poorly reported medical AI tools are common. To close this transparency gap, we developed and piloted a framework to quantify the transparency of medical AI tools with three use cases. Our framework comprises a survey to report on the intended use, training and validation data and processes, ethical considerations, and deployment recommendations. The transparency of each response was scored with either 0, 0.5, or 1 to reflect if the requested information was not, partially, or fully provided. Additionally, we assessed on an analogous three-point scale if the provided responses fulfilled the transparency requirement for a set of trustworthiness criteria from ethical guidelines. The degree of transparency and trustworthiness was calculated on a scale from 0% to 100%. Our assessment of three medical AI use cases pin-pointed reporting gaps and resulted in transparency scores of 67% for two use cases and one with 59%. We report anecdotal evidence that business constraints and limited information from external datasets were major obstacles to providing transparency for the three use cases. The observed transparency gaps also lowered the degree of trustworthiness, indicating compliance gaps with ethical guidelines. All three pilot use cases faced challenges to provide transparency about medical AI tools, but more studies are needed to investigate those in the wider medical AI sector. Applying this framework for an external assessment of transparency may be infeasible if business constraints prevent the disclosure of information. New strategies may be necessary to enable audits of medical AI tools while preserving business secrets.

An Experimental Assessment of Depth Estimation in Transparent and Translucent Scenes for Intel RealSense D415, SR305 and L515

RGB-D cameras have become common in many research fields since these inexpensive devices provide dense 3D information from the observed scene. Over the past few years, the RealSense™ range from Intel^® has introduced new, cost-effective RGB-D sensors with different technologies, more sophisticated in both hardware and software. Models D415, SR305, and L515 are examples of successful cameras launched by Intel^® RealSense™ between 2018 and 2020. These three cameras are different since they have distinct operating principles. Then, their behavior concerning depth estimation while in the presence of many error sources will also be specific. For instance, semi-transparent and scattering media are expected error sources for an RGB-D sensor. The main new contribution of this paper is a full evaluation and comparison between the three Intel RealSense cameras in scenarios with transparency and translucency. We propose an experimental setup involving an aquarium and liquids. The evaluation, based on repeatability/precision and statistical distribution of the acquired depth, allows us to compare the three cameras and conclude that Intel RealSense D415 has overall the best behavior namely in what concerns the statistical variability (also known as precision or repeatability) and also in what concerns valid measurements.

Flexible and Semi-Transparent Silicon Solar Cells as a Power Supply to Smart Contact Lenses

Supplying electric power to wearable IoT devices, particularly smart contact lenses (SCLs), is one of the main obstacles to widespread adoption and commercialization. In the present study, we have successfully designed, fabricated, and characterized semi-transparent, self-supported, and flexible single crystalline silicon solar cells using a single-sided micromachining procedure. Optical, mechanical, and electrical simulations, together with the practical measurements, verify the application of our developed solar cells to be mounted on a limited-footprint and flexible SCL. The 15 μm-thick silicon solar cells conformally fit on a dome-shaped contact lens (ROC = 8 mm) without any mechanical and electrical degradation. This homojunction photovoltaic device containing an array of micro-holes exhibits a V _oc, J _sc, and maximum power density of 504 mV, 6.48 mA cm^-2, and 1.67 mW cm^-2, respectively, at 25% visible light transparency under an AM1.5 one sun condition. Furthermore, the measurements were conducted under low-intensity indoor light conditions and resulted in a maximum power output of 25 and 42 μW cm^-2 for the 50 and 25% transparent solar cells, respectively.

On what basis did Health Canada approve OxyContin in 1996?: A retrospective analysis of regulatory data

The marketing and sale of oxycodone (OxyContin) by Purdue Pharma has commanded a great deal of legal and policy attention due to the drug’s central role in the ongoing overdose crisis. However, little is known about the basis for OxyContin’s approval by regulators, such as Health Canada in 1996. Taking advantage of a recently created online database containing information pertaining to the safety and effectiveness of drugs, we conducted a retrospective analysis of Purdue Pharma’s submission to Health Canada, including both published and unpublished clinical trials. None of the trials sponsored by Purdue Pharma sought to meaningfully assess the risks of misuse or addiction associated with OxyContin. The trials were short in duration (maximum length was 24 days) and only assessed safety and efficacy of a 12-h dosing interval. Also, the two trial reports that explicitly mentioned (but did not formally evaluate) the risk of misuse were not published, making it unclear how Health Canada concluded that there was no risk of misuse. In our view, these findings underscore the need for transparency of not only of clinical trial data, but also the regulator’s interpretation of such data, which is currently lacking in Canada. Furthermore, they call into question why Health Canada’s role in precipitating the overdose crisis has not received greater scrutiny, including in the context of recent litigation surrounding OxyContin.

Tuning Thermal, Morphological, and Physicochemical Properties of Thermoplastic Polyurethanes (TPUs) by the 1,4-Butanediol (BDO)/Dipropylene Glycol (DPG) Ratio

Thermoplastic polyurethanes (TPUs) are versatile polymers presenting a broad range of properties as a result of their countless combination of raw materials—in essence, isocyanates, polyols, and chain extenders. This study highlights the effect of two different chain extenders and their combination on the structure−property relationships of TPUs synthesized by reactive extrusion. The TPUs were obtained from 4,4-diphenylmethane diisocyanate (MDI), polyester diols, and the chain extenders 1,4-butanediol (BDO) and dipropylene glycol (DPG). The BDO/DPG ratios studied were 100/0, 75/25, 50/50, 25/75, and 0/100 wt.%. The TPUs were characterized by size exclusion chromatography (SEC), Fourier-transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), small-angle X-ray scattering (SAXS), UV−vis spectroscopy, and physical-mechanical properties. The results indicate that DPG promotes compatibility between rigid (HS) and flexible (SS) segments of TPUs. Consequently, increasing DPG content (>75 wt.%) reduced the organization of the rigid segments and the degree of phase separation, increasing the polydispersity of the interdomain distance and the transparency in the UV−visible spectrum of the TPUs. Furthermore, increasing DPG content also reduced the amount of hydrogen bonds present in the rigid phase, reducing or extinguishing its glass transition temperature (TgHS) and melting temperature (Tm), and increasing the glass transition temperature of the flexible phase (TgSS). Therefore, increasing DPG content leads to a deterioration in mechanical properties and hydrolysis resistance.

Data rescue: saving environmental data from extinction

Historical and long-term environmental datasets are imperative to understanding how natural systems respond to our changing world. Although immensely valuable, these data are at risk of being lost unless actively curated and archived in data repositories. The practice of data rescue, which we define as identifying, preserving, and sharing valuable data and associated metadata at risk of loss, is an important means of ensuring the long-term viability and accessibility of such datasets. Improvements in policies and best practices around data management will hopefully limit future need for data rescue; these changes, however, do not apply retroactively. While rescuing data is not new, the term lacks formal definition, is often conflated with other terms (i.e. data reuse), and lacks general recommendations. Here, we outline seven key guidelines for effective rescue of historically collected and unmanaged datasets. We discuss prioritization of datasets to rescue, forming effective data rescue teams, preparing the data and associated metadata, and archiving and sharing the rescued materials. In an era of rapid environmental change, the best policy solutions will require evidence from both contemporary and historical sources. It is, therefore, imperative that we identify and preserve valuable, at-risk environmental data before they are lost to science.

Water Quality Measurement and Modelling Based on Deep Learning Techniques: Case Study for the Parameter of Secchi Disk

The Secchi disk is often used to monitor the transparency of water. However, the results of personal measurement are easily affected by subjective experience and objective environment, and it is time-consuming. With the rapid development of computer technology, using image processing technology is more objective and accurate than personal observation. A transparency measurement algorithm is proposed by combining deep learning, image processing technology, and Secchi disk measurement. The white part of the Secchi disk is cropped by image processing. The classification network based on resnet18 is applied to classify the segmentation results and determine the critical position of the Secchi disk. Then, the semantic segmentation network Deeplabv3+ is used to segment the corresponding water gauge at this position, and subsequently segment the characters on the water gauge. The segmentation results are classified by the classification network based on resnet18. Finally, the transparency value is calculated according to the segmentation and classification results. The results from this algorithm are more accurate and objective than that of personal observation. The experiments show the effectiveness of this algorithm.

Replacing bar graphs of continuous data with more informative graphics: Are we making progress?

Recent work has raised awareness about the need to replace bar graphs of continuous data with informative graphs showing the data distribution. The impact of these efforts is not known. The present observational meta-research study examined how often scientists in different fields use various graph types, and assessed whether visualization practices have changed between 2010 and 2020. We developed and validated an automated screening tool, designed to identify bar graphs of counts or proportions, bar graphs of continuous data, bar graphs with dot plots, dot plots, box plots, violin plots, histograms, pie charts, and flow charts. Papers from 23 fields (approximately 1000 papers/field per year) were randomly selected from PubMed Central and screened (n=227998). F1 scores for different graphs ranged between 0.83 and 0.95 in the internal validation set. While the tool also performed well in external validation sets, F1 scores were lower for uncommon graphs. Bar graphs are more often used incorrectly to display continuous data than they are used correctly to display counts or proportions. The proportion of papers that use bar graphs of continuous data varies markedly across fields (range in 2020: 4–58%), with high rates in biochemistry and cell biology, complementary and alternative medicine, physiology, genetics, oncology and carcinogenesis, pharmacology, microbiology and immunology. Visualization practices have improved in some fields in recent years. Fewer than 25% of papers use flow charts, which provide information about attrition and the risk of bias. The present study highlights the need for continued interventions to improve visualization and identifies fields that would benefit most.

Improving Public Trust in Biobanking: Roundtable Discussions from the 2021 ISBER Annual Meeting

Biobanking is a relatively newly recognized and innovative branch of science, which includes the collection of samples and associated data from hospitals, diagnostic centers, and voluntary donations for biomedical and environmental research. It involves diverse stakeholders at the junction of society, science, ethics, law, and politics. A key element in the success of a biobank is the trust and support of public donors, clinicians, and scientists. To achieve trust, it is important to implement strategies that can increase biobank awareness in common people, and different types of communities. Biobank laws and regulations and transparent governance by the biobanks are also crucial to achieving public trust.

Phonological vs. natural gender cues in the acquisition of German by simultaneous and sequential bilinguals (German-Russian)

We investigate German-Russian bilingual children's sensitivity to formal and semantic cues when assigning gender to nouns in German. Across languages, young children have been shown to primarily rely on phonological cues, whereas sensitivity to semantic and syntactic cues increases with age. With its semi-transparent gender assignment system, where both formal and semantic cues are psycho linguistically relevant, German has weak phonological cues compared to other languages, and children have been argued to acquire semantic and phonological rules in tandem. German-Russian bilingual children face the challenge of acquiring two different gender assignment systems simultaneously. We tested 45 bilingual children (ages 4-10 years) and monolingual controls. Results show that the children are clearly sensitive to phonological cues, while semantic cues play a minor role. However, monolingual and bilingual children have different defaulting strategies, with monolinguals defaulting to neuter and bilinguals to feminine gender.

A systematic review of digital technology and innovation and its potential to address anti-corruption, transparency, and accountability in the pharmaceutical supply chain

INTRODUCTION

The urgent need to acquire medical supplies amidst the COVID-19 pandemic has led to bypassing of controls that govern the global pharmaceutical supply chain, increasing the risk of corruption. Hence, promoting anti-corruption, transparency, and accountability (ACTA) in supply chain and procurement has never been more important. The adoption of digital tools, if designed and implemented appropriately, can reduce the risks of corruption.

AREAS COVERED

Following PRISMA guidelines, we conducted an interdisciplinary systematic review of health/medicine, humanities/social sciences, engineering, and computer science literature, with the aims of identifying technologies used for pharmaceutical supply chain and procurement optimization and reviewing whether they address ACTA mechanisms to strengthen pharmaceutical governance. Our review identified four distinct categories of digital solutions: e-procurement and open contracting; track-and-trace technology; anti-counterfeiting technology; and blockchain technology.

EXPERT OPINION

Findings demonstrate an increase in research of technologies to improve pharmaceutical supply chain and procurement functions; however, most technologies are not being leveraged to directly address ACTA or global health outcomes. Some blockchain and RFID technologies incorporated ACTA mechanisms and mentioned specific policy/governance frameworks, but more purposeful linkage is needed. Findings point to the need for targeted policy development and governance to activate these innovative technologies to improve global health .

Electrochemical Transparency of Graphene

In the present study, we used the electrochemical transparency of graphene to show that the direct intercalation of alkali-metal cations is not a prerequisite for the redox reaction of Prussian blue (PB). PB thin films passivated with monolayer graphene still underwent electrochemical redox reactions in the presence of alkali-metal ions (K⁺ or Na⁺) despite the inability of the cations to penetrate the graphene and be incorporated into the PB. Graphene passivation not only preserved the electrochemical activity of the PB but also substantially enhanced the stability of the PB. As a proof of concept, we showed that a transparent graphene electrode covering PB can be used as an excellent hydrogen peroxide transducer, thereby demonstrating the possibility of realizing an electrochemical sensor capable of long-term measurements.

How Reporting Guidelines Improve the Transparency of Biomedical Research

What is scientific quality and how can it be achieved? Recent developments in clinical biomedicine gave prominence to transparency as a new core value for scientific research. Without transparency, other characteristics and values remain unknown. But how can abstract concepts and values be implemented in day-to-day scientific practices and what gets lost on the way? In order to answer this question, this study investigates the role of the PRISMA reporting guideline for writing systematic reviews and meta-analyses. By combining a document analysis and expert interviews with its developers, it attempts to bridge the gap between research practice and current modes of evaluation. Beside showing how the guideline was designed to be applicable and acceptable as a new standard, the analysis revealed crucial distinctions between transparency as an abstract quality goal and its practical implementation in the form of specifically formulated rules. Although PRISMA relies on transparency in order to be meaningful, it blurs the concept in order to circumvent some of its main disadvantages.

An Updated Framework for Industry Funding of Food and Nutrition Research: Managing Financial Conflicts and Scientific Integrity

Although the food and beverage industry plays a critical role in advancing food and nutrition science, industry-funded research is subject to intense scrutiny as a result of various perceived and real biases related to funding sources. To address this, the Institute for the Advancement of Food and Nutrition Sciences (IAFNS) Assembly on Scientific Integrity has updated its Guiding Principles for Funding Food Science and Nutrition Research to provide a modernized framework for minimizing bias and promoting integrity in industry-funded research. Existing best practices for managing conflicts and maintaining trust in science, as well as coverage related to conflicts in industry-funded research, were reviewed to inform the development of the updated Guiding Principles. The updated Guiding Principles continue to provide conflict-of-interest guidelines to protect the integrity and credibility of the scientific record. These updates provide clarification, strengthen the guardrails that separate the funding from the science, and reflect the shift within the scientific community toward increased transparency and open science. If the principles are followed as intended, there should be little reason to dispute the results of industry-funded studies, other than to debate the science itself. This article issues a challenge to the research community to strive for just that.

Assessing Open Science practices in physical activity behaviour change intervention evaluations

Objectives

Concerns on the lack of reproducibility and transparency in science have led to a range of research practice reforms, broadly referred to as 'Open Science'. The extent that physical activity interventions are embedding Open Science practices is currently unknown. In this study, we randomly sampled 100 reports of recent physical activity randomised controlled trial behaviour change interventions to estimate the prevalence of Open Science practices.

Methods

One hundred reports of randomised controlled trial physical activity behaviour change interventions published between 2018 and 2021 were identified, as used within the Human Behaviour-Change Project. Open Science practices were coded in identified reports, including: study pre-registration, protocol sharing, data, materials and analysis scripts sharing, replication of a previous study, open access publication, funding sources and conflict of interest statements. Coding was performed by two independent researchers, with inter-rater reliability calculated using Krippendorff's alpha.

Results

78 of the 100 reports provided details of study pre-registration and 41% provided evidence of a published protocol. 4% provided accessible open data, 8% provided open materials and 1% provided open analysis scripts. 73% of reports were published as open access and no studies were described as replication attempts. 93% of reports declared their sources of funding and 88% provided conflicts of interest statements. A Krippendorff's alpha of 0.73 was obtained across all coding.

Conclusion

Open data, materials, analysis and replication attempts are currently rare in physical activity behaviour change intervention reports, whereas funding source and conflict of interest declarations are common. Future physical activity research should increase the reproducibility of their methods and results by incorporating more Open Science practices.

Pharmaceutical Payments to Authors of Dermatology Guidelines After Publication

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Development of a Guideline to Enhance the Reporting of Concept Mapping Research: Study Protocol

Reporting guidelines are intended to enhance the clarity and transparency of research publications. Concept mapping, a mixed-methods design, has been widely used in health research. Current reporting guidelines for mixed-methods studies are not relevant for concept mapping research. The aim of this study is to develop a reporting guideline for concept mapping research following the EQUATOR network toolkit. Guideline development is in three stages: 1. A systematic review to identify key components of reporting concept mapping research, 2. A concept mapping study involving the key stakeholder groups—researchers, methodologists, peer reviewers, journal editors, statisticians, and people who have participated in concept mapping research—to identify candidate items to include in a reporting guideline, and 3. Development of a draft reporting guideline for concept mapping research. The outcome of the research will be a reporting guideline for concept mapping research.

Building trust in research through information and intent transparency with health information: representative cross-sectional survey of 502 US adults

OBJECTIVE

Participation in healthcare research shapes health policy and practice; however, low trust is a barrier to participation. We evaluated whether returning health information (information transparency) and disclosing intent of data use (intent transparency) impacts trust in research.

MATERIALS AND METHODS

We conducted an online survey with a representative sample of 502 US adults. We assessed baseline trust and change in trust using 6 use cases representing the Social-Ecological Model. We assessed descriptive statistics and associations between trust and sociodemographic variables using logistic and multinomial regression.

RESULTS

Most participants (84%) want their health research information returned. Black/African American participants were more likely to increase trust in research with individual information transparency (odds ratio (OR) 2.06 [95% confidence interval (CI): 1.06-4.34]) and with intent transparency when sharing with chosen friends and family (3.66 [1.98-6.77]), doctors and nurses (1.96 [1.10-3.65]), or health tech companies (1.87 [1.02-3.40]). Asian, Native American or Alaska Native, Native Hawaiian or Pacific Islander, Multirace, and individuals with a race not listed, were more likely to increase trust when sharing with health policy makers (1.88 [1.09-3.30]). Women were less likely to increase trust when sharing with friends and family (0.55 [0.35-0.87]) or health tech companies (0.46 [0.31-0.70]).

DISCUSSION

Participants wanted their health information returned and would increase their trust in research with transparency when sharing health information.

CONCLUSION

Trust in research is influenced by interrelated factors. Future research should recruit diverse samples with lower baseline trust levels to explore changes in trust, with variation on the type of information shared.

Closing the knowledge-action gap in conservation with open science

The knowledge-action gap in conservation science and practice occurs when research outputs do not result in actions to protect or restore biodiversity. Among the diverse and complex reasons for this gap, three barriers are fundamental: knowledge is often unavailable to practitioners and challenging to interpret or difficult to use or both. Problems of availability, interpretability, and useability are solvable with open science practices. We considered the benefits and challenges of three open science practices for use by conservation scientists and practitioners. First, open access publishing makes the scientific literature available to all. Second, open materials (detailed methods, data, code, and software) increase the transparency and use of research findings. Third, open education resources allow conservation scientists and practitioners to acquire the skills needed to use research outputs. The long-term adoption of open science practices would help researchers and practitioners achieve conservation goals more quickly and efficiently and reduce inequities in information sharing. However, short-term costs for individual researchers (insufficient institutional incentives to engage in open science and knowledge mobilization) remain a challenge. We caution against a passive approach to sharing that simply involves making information available. We advocate a proactive stance toward transparency, communication, collaboration, and capacity building that involves seeking out and engaging with potential users to maximize the environmental and societal impact of conservation science.

Developing the RIGHT-COI&F extension for the reporting conflicts of interest and funding in practice guidelines: study protocol

Background

Conflicts of interest (COI) and funding may influence the development of practice guidelines, but there are no internationally endorsed guidelines specifically focusing on the reporting on issues related to COI and funding in practice guidelines. Our aim is to develop an extension of the essential Reporting Items of Practice Guidelines in Healthcare (RIGHT) for COIs and Funding in practice guidelines (i.e., RIGHT-COI&F).

Methods

We will follow the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) network’s toolkit for developing a reporting guideline in six stages: (I) identifying the need for the extension; (II) registering the project and setting up working groups; (III) collecting the initial items; (IV) reaching consensus on the items to be included; (V) revision and formulation of the final checklist; and (VI) dissemination and implementation. We intend to form a multidisciplinary international team of experts to collect and evaluate the items and plan to complete the full reporting guideline in about 2 years.

Discussion

The RIGHT-COI&F statement will help guideline developers improve their reporting of issues related to COIs and funding, and subsequently improve the reporting quality of their guidelines. Journals editors, guideline users and evaluators will benefit from a more complete and transparent reporting of COI.

Trial Registration

We have registered the protocol on the EQUATOR network (https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-other-study-designs/#RIGHT-COI).

Publishing clinical prActice GuidelinEs (PAGE): Recommendations from editors and reviewers

Transparency Ecosystem for Research and Journals in Medicine (TERM) Working Group summarized the essential recommendations that should be considered to review and publish a high-quality guideline. These recommendations from editors and reviewers included the ten components of essential requirements: systematic review of existing relevant guidelines, guideline registration, guideline protocol, stakeholders, conflicts of interest, clinical questions, systematic reviews, recommendation consensus, guideline reporting, and external review. TERM Working Group abbreviates them as PAGE (essential requirements for Publishing clinical prActice GuidelinEs), recommends guideline authors, editors, and peer reviewers use them for high-quality guidelines.

Challenges in Interpreting Obstetrics and Gynecology Literature

The field of obstetrics and gynecology is constantly replenished with the newest research findings. In an era of rapidly available study publications, there are a number of challenges to interpreting the obstetrics and gynecology literature. Common pitfalls include the over reliance on the dichotomized P-value, lack of transparency, bias in study reporting, limitations of resources, absence of standardized practices and outcomes in study design, and the rare concerns for data integrity. We review these predominant challenges and their potential solutions, in interpreting the obstetrics and gynecology literature.

Open energy system modelling to support the European Green Deal

Energy models are used to explore decarbonisation pathways and potential future energy systems. In this editorial, we comment on the importance of energy system modelling and open tools to inform policymaking in the context of the European Green Deal. We also summarise the seven contributions to the special collection on Energy Systems Modelling, among which are papers that have been presented at the Energy Modelling Platform for Europe (EMP-E) 2021 conference. The presented research advances current modelling approaches and supports energy modelling with open tools and datasets.

Cellulose-Based Scattering Enhancers for Light Management Applications

To manipulate the light-matter interaction effectively, we often rely on high refractive index inorganic nanoparticles. Such materials are contained essentially in everything that looks colorful or white: from paints to coatings but also in processed food, toothpaste, and cosmetic products. As these nanoparticles can accumulate in the human body and environment, there is a strong need to replace them with more biocompatible counterparts. In this work, we introduce various types of cellulose-based microparticles (CMPs) of four sizes with optimized dimensions for efficient light scattering that can replace traditional inorganic particles. We demonstrate that the produced materials can be exploited as highly efficient scattering enhancers, with designed optical performance. Finally, exploiting these cellulose colloids, we fabricated scattering materials and high transmittance/haze films with record performances with respect to the state-of-the-art values. The renewable and biocompatible nature of our systems, combined with their excellent optical properties, allows for the use of our cellulose-based particles in paints, LEDs, and solar cell devices and especially in applications where the biocompatibility of the component is essential, such as in food and pharmaceutical coatings.

Citizens in Search for a Place in the Digital Health Data Space: A Case Study

BACKGROUND

Digital health solutions have been omnipresent in policy agendas. However, we still need to better understand how citizens experience these developments and, more specifically, how citizens would ideally want such solutions to look like.

OBJECTIVE

We explore the needs and concerns citizens expressed in different phases of the co-creation process for a prototype of a citizen-centred health data platform within a large-scale European project: Smart4Health.

METHOD

We follow a qualitative approach in our analysis of 9 discussion groups in addition to a diverse set of 49 qualitative interviews with citizens and health care professionals.

RESULTS

We show how citizens identify the positive potential of health data infrastructures and how they relate digital health to wider developments in contemporary societies. We then outline citizens' concerns that potentially prevent them from becoming users and thus destabilize the policy vision of digital health.

CONCLUSION

Four preconditions need to be met for citizens to find their place within a digital health data environment: transparency/trust, infrastructural literacy, digital justice, and a careful consideration of the distribution of responsibilities.

Effect of synthesized sulfonate derivatives as nucleating agents on crystallization behavior of poly(lactic acid)

The improvement of the crystallization of poly(lactic acid) (PLA) is one of the key areas to allow PLA to perform better at higher temperature and load bearing. Due to its slow crystallization rate, either organic or inorganic nucleating agents (NAs) can be used to improve the crystallization rate of PLA. In the case of organic NAs, aromatic sulfonate salt and bisamide compounds are promising ones because they can control better clarity. The aim of this work was to study the crystallization behavior of PLA using as-synthesized dimethyl 5-sulfoisophthalate sodium salt (SSIPA) as a nucleating agent in comparison with the commercial sulfonate salt (LAK-301). Two grades of PLA (PLA L105 and PLA 3251D) were used in this study. PLA samples were prepared by internal mixer and compression molding. All samples were investigated by DSC and POM. The results from DSC showed that after introducing the nucleating agents into PLA, the crystallinity in all samples was improved. The highest crystallinity at 57.48% was obtained from PLA L105/SSIPA1.0. Isothermal crystallization kinetics showed the improvement in overall crystallization rate of PLA with nucleating agents. The lowest half time crystallization obtained was 1.19 min for PLA L105/SSIPA1.0 at 135 °C. The results from POM indicated the substantial increase of the nucleus density and smaller spherulite size upon adding nucleating agents.

Perceptual scale for transparency: Common fate overrides geometrical and color cues

Objects that pass light through are considered transparent, and we generally expect that the light coming out will match the color of the object. However, when the object is placed on a colored surface, the light coming back to our eyes becomes a composite of surface, illumination, and transparency properties. Despite that, we can often perceive separate overlaid and overlaying layers differing in colors. How neurons separate the information to extract the transparent layer remains unknown, but the physical characteristics of transparent filters generate geometrical and color features in retinal images, which could provide cues for separating layers. We estimated the relative importance of such cues in a perceptual scale for transparency, using stimuli in which X- or T-junctions, different relative motions, and consistent or inconsistent colors cooperated or competed in forced-preference psychophysics experiments. Maximum-likelihood Thurstone scaling revealed that motion increased transparency for X-junctions, but decreased transparency for T-junctions by creating the percept of an opaque patch. However, if the motion of a filter uncovered a dynamically changing but stationary pattern, sharing a common fate with the surround but forming T-junctions, the probability of seeing transparency was almost as high as for moving X-junctions, despite the stimulus being physically improbable. In addition, geometric cues overrode color inconsistency to a great degree. Finally, a linear model of transparency perception as a function of relative motions between filter, overlay, and surround layers, contour continuation, and color consistency, quantified a hierarchy of latent influences on when the filter is seen as a separate transparent layer.

Transparent, Photothermal, and Icephobic Surfaces via Layer-by-Layer Assembly

Icing and frosting on transparent surfaces compromise visibility on various optical equipment and transparent infrastructures. It remains challenging to fabricate energy-saving coatings for harvesting solar energy while maintaining high transparency. Here, transparent, photothermic, and icephobic composite surfaces composed of photothermal nanomaterials and polyelectrolytes via layer-by-layer assembly are designed and constructed. The positively-charged polypyrrole nanoparticles and negatively-charged poly(acrylic acid) are assembled as exemplary materials via electrostatic attractions. The optically transparent photothermal coatings are successfully fabricated and exhibited photothermal capabilities and light-transmittance performance. Among the various coatings applied, the seven-bilayer coating can increase the temperature by 35 °C under 1.9-sun illumination, maintaining high transmittance (>60%) of visible light. With sunlight illumination at subzero temperatures (> -35 °C), the coatings show pronounced capabilities to inhibit freezing, melt accumulated frost, and decrease ice adhesion. Precisely, the icephobic surfaces remain free of frost at -35 °C as long as sunlight illumination is present; the accumulated frost melts rapidly within 300 s. The ice adhesion strength decreases to ≈0 kPa as the melted water acts as a lubricant. Furthermore, the negatively-charged graphene oxide and positively-charged poly(diallyldimethylammonium chloride) show their material diversity applicable in the coating fabrication.

How transparency modulates trust in artificial intelligence

The study of human-machine systems is central to a variety of behavioral and engineering disciplines, including management science, human factors, robotics, and human-computer interaction. Recent advances in artificial intelligence (AI) and machine learning have brought the study of human-AI teams into sharper focus. An important set of questions for those designing human-AI interfaces concerns trust, transparency, and error tolerance. Here, we review the emerging literature on this important topic, identify open questions, and discuss some of the pitfalls of human-AI team research. We present opposition (extreme algorithm aversion or distrust) and loafing (extreme automation complacency or bias) as lying at opposite ends of a spectrum, with algorithmic vigilance representing an ideal mid-point. We suggest that, while transparency may be crucial for facilitating appropriate levels of trust in AI and thus for counteracting aversive behaviors and promoting vigilance, transparency should not be conceived solely in terms of the explainability of an algorithm. Dynamic task allocation, as well as the communication of confidence and performance metrics—among other strategies—may ultimately prove more useful to users than explanations from algorithms and significantly more effective in promoting vigilance. We further suggest that, while both aversive and appreciative attitudes are detrimental to optimal human-AI team performance, strategies to curb aversion are likely to be more important in the longer term than those attempting to mitigate appreciation. Our wider aim is to channel disparate efforts in human-AI team research into a common framework and to draw attention to the ecological validity of results in this field.

Recent advances in artificial intelligence (AI) and machine learning have brought the study of human-AI (HAI) teams into sharper focus. An important set of questions for those designing HAI interfaces concerns trust—specifically, human trust in the AI systems with which they form teams. We review the literature on how perceiving an AI making mistakes violates trust and how such violations might be repaired. In doing so, we discuss the role played by various forms of algorithmic transparency in the process of trust repair, including explanations of algorithms, uncertainty estimates, and performance metrics.

Improving transparency-A call to include social housing information in biomedical research articles involving nonhuman primates

The social setting of animal subjects in the research environment has known effects on a variety of dependent measures used in biomedical research. Proper evaluation of the robustness of published research is dependent upon transparent, detailed, and accurate reporting of research methods, including the animals' social housing conditions. However, to date, most research articles utilizing nonhuman primates (NHPs) provide only partial data on this topic, hampering transparency, and reproducibility. Therefore, we call for the inclusion of information pertaining to the social aspects of the animals' housing conditions in publications involving NHPs to improve transparency. We argue that including this information in scientific publications is crucial for the interpretation of research findings in the appropriate context and for understanding unexplained variability in study findings. Finally, the inclusion of this information in publications will additionally familiarize scientists with how other researchers conducting similar studies are housing their animals and will encourage them to consider the implications of various housing conditions on their research outcomes.

Trends and variation in data quality and availability on the European Union Clinical Trials Register: A cross-sectional study

BACKGROUND/AIMS

The European Union Clinical Trials Register is a public facing portal containing information on trials of medicinal products conducted under the purview of the European Union regulatory system. As of September 2021, the registry holds information on over 40,000 trials. Given its distinct regulatory purpose, and results reporting requirements, the European Union Clinical Trials Register should be a valuable open-source hub for trial information. Past work examining the European Union Clinical Trials Register has suggested that data quality on the registry may be lacking. We therefore set out to examine the quality and availability of trial data on the registry with a focus on areas that fall under the authority of regulators in each European Union/European Economic Area country.

METHODS

Using data scraped from the full European Union Clinical Trials Register public dataset, we examined the extent of issues with three areas of trial data availability linked to European Union regulations. We examined whether there is evidence for missing registration of protocols in the public database, whether information on the completion of clinical trials is being made available and how often the results of trials are posted to the registry. We assessed each area overall, and examined variation between national regulators and over time.

RESULTS

Major issues with the availability of expected protocols and information on trial completion were focused in a few countries. Overall, when comparing enrolment countries from tabular results to available registrations, 26,932 of 31,118 (86.5%) expected protocols were available and 22 of 30 (73%) countries had over 90% of expected protocols available. The majority of missing protocols, totalling 2764 (66%), were from just three countries: France, Norway and Poland. Evidence for this issue is further supported by data on trends in new registrations by country over time. Low availability of data on trial completion is also most pronounced in a minority of countries, like Spain and the Netherlands, with consistent trends for missingness over time. Finally, overall results availability is substantially worse among the 23,623 trials with a single registered European Union protocol (n = 6259, 26.5%) compared to 13,897 of those taking place in multiple countries (n = 8423, 60.6%). Reporting for single-protocol trials was consistently low across both time and location.

CONCLUSION

Deficiencies in the public availability of trial protocols, trial completion information and summary results complicate the utility of the European Union Clinical Trials Register for research, transparency and accountability efforts. Users of the registry would benefit from a more complete and accurate accounting of the European research environment via the official European Union registry. We recommend regulators at the national and pan-national level undertake routine audits of approved trials to ensure national-level issues are proactively and transparently identified, documented and addressed.

Public understanding of science: Communicating in the midst of a pandemic

While Covid-19 created unprecedented transparency with real-time reporting across the globe, the pandemic's politically charged environment made public communication of scientific information particularly challenging. Scientists, as authoritative voices, were thrust into the public eye to explain the evidence amid uncertainty, a changing virus, distrust in government and concerns about their self-interest and hidden agendas. Honest communication that provides the public with enough easy-to-understand information to make up their own minds is essential during the pandemic; the public deserves nothing less.

Graphene as a Transparent Conductive Electrode in GaN-Based LEDs

Graphene combines high conductivity (sheet resistance down to a few hundred Ω/sq and even less) with high transparency (>90%) and thus exhibits a huge application potential as a transparent conductive electrode in gallium nitride (GaN)-based light-emitting diodes (LEDs), being an economical alternative to common indium-based solutions. Here, we present an overview of the state-of-the-art graphene-based transparent conductive electrodes in GaN-based LEDs. The focus is placed on the manufacturing progress and the resulting properties of the fabricated devices. Transferred as well as directly grown graphene layers are considered. We discuss the impact of graphene-based transparent conductive electrodes on current spreading and contact resistance, and reveal future challenges and perspectives on the use of graphene in GaN-based LEDs.

Defining AMIA's artificial intelligence principles

Recent advances in the science and technology of artificial intelligence (AI) and growing numbers of deployed AI systems in healthcare and other services have called attention to the need for ethical principles and governance. We define and provide a rationale for principles that should guide the commission, creation, implementation, maintenance, and retirement of AI systems as a foundation for governance throughout the lifecycle. Some principles are derived from the familiar requirements of practice and research in medicine and healthcare: beneficence, nonmaleficence, autonomy, and justice come first. A set of principles follow from the creation and engineering of AI systems: explainability of the technology in plain terms; interpretability, that is, plausible reasoning for decisions; fairness and absence of bias; dependability, including "safe failure"; provision of an audit trail for decisions; and active management of the knowledge base to remain up to date and sensitive to any changes in the environment. In organizational terms, the principles require benevolence-aiming to do good through the use of AI; transparency, ensuring that all assumptions and potential conflicts of interest are declared; and accountability, including active oversight of AI systems and management of any risks that may arise. Particular attention is drawn to the case of vulnerable populations, where extreme care must be exercised. Finally, the principles emphasize the need for user education at all levels of engagement with AI and for continuing research into AI and its biomedical and healthcare applications.

The Role of Specular Reflections and Illumination in the Perception of Thickness in Solid Transparent Objects

Specular reflections and refractive distortions are complex image properties of solid transparent objects, but despite this complexity, we readily perceive the 3D shapes of these objects (e.g., glass and clear plastic). We have found in past work that relevant sources of scene complexity have differential effects on 3D shape perception, with specular reflections increasing perceived thickness, and refractive distortions decreasing perceived thickness. In an object with both elements, such as glass, the two optical properties may complement each other to support reliable perception of 3D shape. We investigated the relative dominance of specular reflection and refractive distortions in the perception of shape. Surprisingly, the ratio of specular reflection to refractive component was almost equal to that of ordinary glass and ice, which promote correct percepts of 3D shape. The results were also explained by the variance in local RMS contrast in stimulus images but may depend on overall luminance and contrast of the surrounding light field.

Reconciling the Conflict between Optical Transparency and Fouling Resistance with a Nanowrinkled Surface Inspired by Zebrafish's Cornea

Surface topography has been demonstrated as an effective nonchemical strategy for controlling the fouling resistance of a surface, but its impact on optical transparency remains a barrier to the application of this strategy in optical materials. To reconcile the conflicting effects of surface topography on optical transparency and fouling resistance, here we study the optical properties and antifouling performance of nanowrinkled surfaces inspired by the corneal surface of zebrafish (Danio rerio). Experimental and numerical analyses demonstrate that a good compromise between optical transparency and antifouling efficacy can be achieved by wavy nanowrinkles with a characteristic wavelength of 800 nm and an amplitude of 100 nm. In particular, the optimal wrinkled surface under study can reduce biofouling by up to 96% in a single-species (Pseudoalteromonas sp.) bacterial settlement assay in the laboratory and 89% in a field test while keeping the total transmittance above 0.98 and haze below 0.04 underwater. Moreover, our nanowrinkled surface also exhibits excellent resistance against contamination by inorganic particles. This work provides a nonchemical strategy for achieving the coexistence of optical transparency and fouling resistance on one single material, which implies significant application potential in various optical devices and systems, such as antibacterial contact lenses and self-cleaning solar panels.

Explicability of artificial intelligence in radiology: Is a fifth bioethical principle conceptually necessary?

Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black-box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of general ethical frameworks to clinical decision-making entails conceptual questions: Is explicability a free-standing principle? Is it already covered by the well-established four bioethical principles? Or is it an independent value that needs to be recognized as such in medical practice? We discuss these questions in a conceptual-ethical analysis, which builds upon the findings of an empirical document analysis. On the example of the medical specialty of radiology, we analyze the position of radiological associations on the ethical use of medical AI. We address three questions: Are there references to explicability or a similar concept? What are the reasons for such inclusion? Which ethical concepts are referred to?

Good clinical practice improves rigor and transparency: Lessons from the ACTIVE trial

Clinical trials are governed by principles of good clinical practice (GCP), which can strengthen the achievement of rigor, reproducibility, and transparency in scientific research. Rigor, reproducibility, and transparency are key for producing findings with greater certainty. Clinical trials are closely supervised, often by a clinical trial coordinating center, data safety and monitoring board, and a funding agency, with policies that are a manifestation of GCP and support rigor, reproducibility, and transparency. The multisite Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study is an example clinical trial of relevance to a psychology and aging audience that utilized many protocols that can be applied to single-laboratory designs, including a manualized protocol with accompanying scientific rationale, predefined analysis plans, standardization of procedures across field sites, assurance of competence of study staff in study procedures, transparent coding/entry/transmittal of data, regular quality assurance, and open publication of data. Despite substantial resource discrepancies between the two, single-laboratory studies can model the GCP principles utilized in large clinical trials to provide an excellent foundation for rigor, reproducibility, and transparency. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Foundational Considerations for Artificial Intelligence Using Ophthalmic Images

IMPORTANCE

The development of artificial intelligence (AI) and other machine diagnostic systems, also known as software as a medical device, and its recent introduction into clinical practice requires a deeply rooted foundation in bioethics for consideration by regulatory agencies and other stakeholders around the globe.

OBJECTIVES

To initiate a dialogue on the issues to consider when developing a bioethically sound foundation for AI in medicine, based on images of eye structures, for discussion with all stakeholders.

EVIDENCE REVIEW

The scope of the issues and summaries of the discussions under consideration by the Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group, as first presented during the Collaborative Community on Ophthalmic Imaging inaugural meeting on September 7, 2020, and afterward in the working group.

FINDINGS

Artificial intelligence has the potential to improve health care access and patient outcome fundamentally while decreasing disparities, lowering cost, and enhancing the care team. Nevertheless, substantial concerns exist. Bioethicists, AI algorithm experts, as well as the Food and Drug Administration and other regulatory agencies, industry, patient advocacy groups, clinicians and their professional societies, other provider groups, and payors (i.e., stakeholders) working together in collaborative communities to resolve the fundamental ethical issues of nonmaleficence, autonomy, and equity are essential to attain this potential. Resolution impacts all levels of the design, validation, and implementation of AI in medicine. Design, validation, and implementation of AI warrant meticulous attention.

CONCLUSIONS AND RELEVANCE

The development of a bioethically sound foundation may be possible if it is based in the fundamental ethical principles of nonmaleficence, autonomy, and equity for considerations for the design, validation, and implementation for AI systems. Achieving such a foundation will be helpful for continuing successful introduction into medicine before consideration by regulatory agencies. Important improvements in accessibility and quality of health care, decrease in health disparities, and lower cost thereby can be achieved. These considerations should be discussed with all stakeholders and expanded on as a useful initiation of this dialogue.

Low statistical power and overestimated anthropogenic impacts, exacerbated by publication bias, dominate field studies in global change biology

Field studies are essential to reliably quantify ecological responses to global change because they are exposed to realistic climate manipulations. Yet such studies are limited in replicates, resulting in less power and, therefore, potentially unreliable effect estimates. Furthermore, while manipulative field experiments are assumed to be more powerful than non-manipulative observations, it has rarely been scrutinized using extensive data. Here, using 3847 field experiments that were designed to estimate the effect of environmental stressors on ecosystems, we systematically quantified their statistical power and magnitude (Type M) and sign (Type S) errors. Our investigations focused upon the reliability of field experiments to assess the effect of stressors on both ecosystem's response magnitude and variability. When controlling for publication bias, single experiments were underpowered to detect response magnitude (median power: 18%-38% depending on effect sizes). Single experiments also had much lower power to detect response variability (6%-12% depending on effect sizes) than response magnitude. Such underpowered studies could exaggerate estimates of response magnitude by 2-3 times (Type M errors) and variability by 4-10 times. Type S errors were comparatively rare. These observations indicate that low power, coupled with publication bias, inflates the estimates of anthropogenic impacts. Importantly, we found that meta-analyses largely mitigated the issues of low power and exaggerated effect size estimates. Rather surprisingly, manipulative experiments and non-manipulative observations had very similar results in terms of their power, Type M and S errors. Therefore, the previous assumption about the superiority of manipulative experiments in terms of power is overstated. These results call for highly powered field studies to reliably inform theory building and policymaking, via more collaboration and team science, and large-scale ecosystem facilities. Future studies also require transparent reporting and open science practices to approach reproducible and reliable empirical work and evidence synthesis.

Improving Diabetes-Related Biomedical Literature Exploration in the Clinical Decision-making Process via Interactive Classification and Topic Discovery: Methodology Development Study

Background

The amount of available textual health data such as scientific and biomedical literature is constantly growing and becoming more and more challenging for health professionals to properly summarize those data and practice evidence-based clinical decision making. Moreover, the exploration of unstructured health text data is challenging for professionals without computer science knowledge due to limited time, resources, and skills. Current tools to explore text data lack ease of use, require high computational efforts, and incorporate domain knowledge and focus on topics of interest with difficulty.

Objective

We developed a methodology able to explore and target topics of interest via an interactive user interface for health professionals with limited computer science knowledge. We aim to reach near state-of-the-art performance while reducing memory consumption, increasing scalability, and minimizing user interaction effort to improve the clinical decision-making process. The performance was evaluated on diabetes-related abstracts from PubMed.

Methods

The methodology consists of 4 parts: (1) a novel interpretable hierarchical clustering of documents where each node is defined by headwords (words that best represent the documents in the node), (2) an efficient classification system to target topics, (3) minimized user interaction effort through active learning, and (4) a visual user interface. We evaluated our approach on 50,911 diabetes-related abstracts providing a hierarchical Medical Subject Headings (MeSH) structure, a unique identifier for a topic. Hierarchical clustering performance was compared against the implementation in the machine learning library scikit-learn. On a subset of 2000 randomly chosen diabetes abstracts, our active learning strategy was compared against 3 other strategies: random selection of training instances, uncertainty sampling that chooses instances about which the model is most uncertain, and an expected gradient length strategy based on convolutional neural networks (CNNs).

Results

For the hierarchical clustering performance, we achieved an F1 score of 0.73 compared to 0.76 achieved by scikit-learn. Concerning active learning performance, after 200 chosen training samples based on these strategies, the weighted F1 score of all MeSH codes resulted in a satisfying 0.62 F1 score using our approach, 0.61 using the uncertainty strategy, 0.63 using the CNN, and 0.45 using the random strategy. Moreover, our methodology showed a constant low memory use with increased number of documents.

Conclusions

We proposed an easy-to-use tool for health professionals with limited computer science knowledge who combine their domain knowledge with topic exploration and target specific topics of interest while improving transparency. Furthermore, our approach is memory efficient and highly parallelizable, making it interesting for large Big Data sets. This approach can be used by health professionals to gain deep insights into biomedical literature to ultimately improve the evidence-based clinical decision making process.

Lifting the Sustainability of Modified Pet-Based Multilayer Packaging Material with Enhanced Mechanical Recycling Potential and Processing

Sustainability and recyclability are among the main driving forces in the plastics industry, since the pressure on crude oil resources and the environment is increasing. The aim of this research is to develop a sustainable thermoformable multilayer food packaging, based on co-polyesters, which is suitable for hot-fill applications and allows for recycling in a conventional waste stream. As a polymer material for the outer layer, we selected a modified polyethylene terephthalate (PETM), which is an amorphous co-polyester with a high glass transition temperature (±105 °C) and thus high thermal stability and transparency. The inner layer consists of 1,4-cyclohexylene dimethanol-modified polyethylene terephthalate (PETg), which is allowed to be recycled in a PET stream. Multilayers with a total thickness of 1 mm and a layer thickness distribution of 10/80/10 have been produced. To test the recyclability, sheets which contained 20% and 50% regrind of the initial multilayer in their middle PETg layer have been produced as well. The sheet produced from virgin pellets and the one containing 20% regrind in the middle layer showed no visible haze. This was not the case for the one containing 50% regrind in the middle layer, which was confirmed by haze measurements. The hot-fill test results showed no shrinkage or warpage for the multilayer trays for all temperatures applied, namely 95, 85, 75 and 65 °C. This is a remarkable improvement compared to pure PETg trays, which show a visible deformation after exposure to hot-fill conditions of 95 °C and 85 °C.

A 'patient-industry complex'? Investigating the financial dependency of UK patient organisations on drug company funding

We examined the minimum extent of dependency of UK patient organisations on pharmaceutical industry funding using drug company disclosure reports and patient organisation financial accounts from 2012 to 2016. We used linear regression to explain the overall share of industry funding ('general dependency') and top donor funding ('company-specific dependency') in organisations' income. Predictors included patient organisations' goal; having members and volunteers; geographical scope of activity; headquarter location; expenditure/income ratio; and disease area. The prevalent low levels of general dependency (IQR, 0.1%-6.0%) and company-specific dependency (IQR, 0.1%-4.3%) made a widespread capture of patient organisations unlikely, though only if one excludes the possibility of significant payment under-reporting. However, organisations with considerably higher dependency than others might be more prone to co-optation by industry. Of the 398 organisations, 18 (4.5%) and 8 (2.0%) had general and company-specific financial dependency over 50%, respectively. However, the shares of outliers exceeding the third quartile plus 1.5 times IQR were 51 (12.8%) and 56 (14.1%) for each dependency type. Certain characteristics including activity profile (advocacy) or indicating limited access to resources (remote location) made organisations vulnerable to developing financial dependency. Future research should examine both financial and non-financial links between the two sides and their impact on patient organisations' activity.