Designing a Clinician-Centered Wearable Data Dashboard (CarePortal): Participatory Design Study

BACKGROUND

The recent growth of eHealth is unprecedented, especially after the COVID-19 pandemic. Within eHealth, wearable technology is increasingly being adopted because it can offer the remote monitoring of chronic and acute conditions in daily life environments. Wearable technology may be used to monitor and track key indicators of physical and psychological stress in daily life settings, providing helpful information for clinicians. One of the key challenges is to present extensive wearable data to clinicians in an easily interpretable manner to make informed decisions.

OBJECTIVE

The purpose of this research was to design a wearable data dashboard, named CarePortal, to present analytic visualizations of wearable data that are meaningful to clinicians. The study was divided into 2 main research objectives: to understand the needs of clinicians regarding wearable data interpretation and visualization and to develop a system architecture for a web application to visualize wearable data and related analytics.

METHODS

We used a wearable data set collected from 116 adolescent participants who experienced trauma. For 2 weeks, participants wore a Microsoft Band that logged physiological sensor data such as heart rate (HR). A total of 834 days of HR data were collected. To design the CarePortal dashboard, we used a participatory design approach that interacted directly with clinicians (stakeholders) with backgrounds in clinical psychology and neuropsychology. A total of 8 clinicians were recruited from the Rhode Island Hospital and the University of Massachusetts Memorial Health. The study involved 5 stages of participatory workshops and began with an understanding of the needs of clinicians. A User Experience Questionnaire was used at the end of the study to quantitatively evaluate user experience. Physiological metrics such as daily and hourly maximum, minimum, average, and SD of HR and HR variability, along with HR-based activity levels, were identified. This study investigated various data visualization graphing methods for wearable data, including radar charts, stacked bar plots, scatter plots combined with line plots, simple bar plots, and box plots.

RESULTS

We created a CarePortal dashboard after understanding the clinicians' needs. Results from our workshops indicate that overall clinicians preferred aggregate information such as daily HR instead of continuous HR and want to see trends in wearable sensor data over a period (eg, days). In the User Experience Questionnaire, a score of 1.4 was received, which indicated that CarePortal was exciting to use (question 5), and a similar score was received, indicating that CarePortal was the leading edge (question 8). On average, clinicians reported that CarePortal was supportive and can be useful in making informed decisions.

CONCLUSIONS

We concluded that the CarePortal dashboard integrated with wearable sensor data visualization techniques would be an acceptable tool for clinicians to use in the future.

Historical trends in reporting effect sizes in clinical neuropsychology journals: A call to venture beyond the results section

OBJECTIVE

For decades, quantitative psychologists have recommended that authors report effect sizes to convey the magnitude and potential clinical relevance of statistical associations. However, fewer than one-third of neuropsychology articles published in the early 2000s reported effect sizes. This study re-examines the frequency and extent of effect size reporting in neuropsychology journal articles by manuscript section and over time.

METHODS

A sample of 326 empirical articles were drawn from 36 randomly selected issues of six neuropsychology journals at 5-year intervals between 1995 and 2020. Four raters used a novel, reliable coding system to quantify the extent to which effect sizes were included in the major sections of all 326 articles.

RESULTS

Findings showed medium-to-large increases in effect size reporting in the Methods and Results sections of neuropsychology journal articles that plateaued in recent years; however, there were only very small and nonsignificant changes in effect size reporting in the Abstract, Introduction, and Discussion sections.

CONCLUSIONS

Authors in neuropsychology journals have markedly improved their effect size reporting in the core Methods and Results sections, but are still unlikely to consider these valuable metrics when motivating their study hypotheses and interpreting the conceptual and clinical implications of their findings. Recommendations are provided to encourage more widespread integration of effect sizes in neuropsychological research.

Use of Research in the Nursing Practice: from Statistical Significance to Clinical Significance

Objective

Within the context of evidence-based practice, this article exposes the reflection on the understanding and usefulness of the information provided by the research findings shared in reports and research publications, exposing differences based on the interpretation of statistical significance and clinical significance.

Content synthesis

Basic aspects of the meaning and use of the information reported by research on p value (statistical significance) and the value and usefulness of these results are analyzed and exemplified, contrasting the value for the practice of an additional judgment on clinical significance. In addition to establishing conceptual differences, the need is highlighted for nurses to have the competencies to differentiate and apply each of them according to the clinical contexts of their potential implementation.

Conclusion

The real usefulness of research about interventions within the context of nursing care is given by its real application and reach for the practice and benefit for patients. For this to occur, nurses must interpret adequately the information provided by scientific publications and other research reports.

Making Sense of Composite Endpoints in Clinical Research

Multiple drugs currently used in clinical practice have been approved by regulatory agencies based on studies that utilize composite endpoints. Composite endpoints are appealing because they reduce sample size requirements, follow-up periods, and costs. However, interpreting composite endpoints can be challenging, and their misuse is not uncommon. Incorrect interpretation of composite outcomes can lead to misleading conclusions that impact patient care. To correctly interpret composite outcomes, several important questions should be considered. Are the individual components of the composite outcome equally important to patients? Did the more and less important endpoints occur with similar frequency? Do the component endpoints exhibit similar relative risk reductions? If these questions receive affirmative answers, the use and interpretation of the composite endpoint would be appropriate. However, if any component of the composite endpoint fails to satisfy the aforementioned criteria, interpretation can become difficult, necessitating additional steps. Regulatory agencies acknowledge these challenges and have specific considerations when approving drugs based on studies employing composite endpoints. In conclusion, composite endpoints are valuable tools for evaluating the efficacy and net clinical benefit of interventions; however, cautious interpretation is advised.

Assessing the quality and making appropriate use of historical negative control data: A report of the International Workshop on Genotoxicity Testing (IWGT)

Historical negative control data (HCD) have played an increasingly important role in interpreting the results of genotoxicity tests. In particular, Organisation for Economic Co-operation and Development (OECD) genetic toxicology test guidelines recommend comparing responses produced by exposure to test substances with the distribution of HCD as one of three criteria for evaluating and interpreting study results (referred to herein as "Criterion C"). Because of the potential for inconsistency in how HCD are acquired, maintained, described, and used to interpret genotoxicity testing results, a workgroup of the International Workshops for Genotoxicity Testing was convened to provide recommendations on this crucial topic. The workgroup used example data sets from four in vivo tests, the Pig-a gene mutation assay, the erythrocyte-based micronucleus test, the transgenic rodent gene mutation assay, and the in vivo alkaline comet assay to illustrate how the quality of HCD can be evaluated. In addition, recommendations are offered on appropriate methods for evaluating HCD distributions. Recommendations of the workgroup are: When concurrent negative control data fulfill study acceptability criteria, they represent the most important comparator for judging whether a particular test substance induced a genotoxic effect. HCD can provide useful context for interpreting study results, but this requires supporting evidence that (i) HCD were generated appropriately, and (ii) their quality has been assessed and deemed sufficiently high for this purpose. HCD should be visualized before any study comparisons take place; graph(s) that show the degree to which HCD are stable over time are particularly useful. Qualitative and semi-quantitative assessments of HCD should also be supplemented with quantitative evaluations. Key factors in the assessment of HCD include: (i) the stability of HCD over time, and (ii) the degree to which inter-study variation explains the total variability observed. When animal-to-animal variation is the predominant source of variability, the relationship between responses in the study and an HCD-derived interval or upper bounds value (i.e., OECD Criterion C) can be used with a strong degree of confidence in contextualizing a particular study's results. When inter-study variation is the major source of variability, comparisons between study data and the HCD bounds are less useful, and consequentially, less emphasis should be placed on using HCD to contextualize a particular study's results. The workgroup findings add additional support for the use of HCD for data interpretation; but relative to most current OECD test guidelines, we recommend a more flexible application that takes into consideration HCD quality. The workgroup considered only commonly used in vivo tests, but it anticipates that the same principles will apply to other genotoxicity tests, including many in vitro tests.

Discussion of Annotated Research Articles Results in Increases in Scientific Literacy within a Cell Biology Course

As the amount and complexity of scientific knowledge continues to grow, it is essential to educate scientifically literate citizens who can comprehend the process of science and the implications of technological advances. This is especially important when educating science, technology, engineering, and mathematics (STEM) college students, since they may play a central role in the future of scientific research and its communication. A central part of decoding and interpreting scientific information is the ability to analyze scientific research articles. For this reason, many different approaches for reading scientific research articles have been developed and published. Despite the availability of numerous ways of analyzing scientific research articles, biology students can face challenges that may prevent them from fully comprehending the text. We sought to address student challenges with science vocabulary and content knowledge by adding structural supports to in-classroom article discussions through the use of annotated articles from the Science in the Classroom initiative. We describe the pedagogical approach used for discussing scientific research articles within a required biology course. In this context, we found that students' scientific literacy skills increased at the end of the semester. We also found that, for each article discussed, the majority of students could interpret graphical representations of article results and that they could identify and comprehend components of the experimental design of the study.

Reporting of absolute and relative risk measures in oral health and cardiovascular events studies: A systematic review

OBJECTIVES

To understand the magnitude of risk of health events, such as cardiovascular diseases (CVD), related to poor oral health, both relative and absolute risk measures should be reported. Our aim was to investigate the extent to which absolute and relative measures of risk are reported in longitudinal studies that assess the association between oral health indicators (OHIs) and CVD.

METHODS

A systematic search of longitudinal studies investigating the association of any OHI with CVD was carried out using the Embase, Medline and Cochrane library databases. The search covered each database from its inception date to August 2021. Data about reporting relative and absolute risks of the relationship between CVD and OHI from the abstract were extracted. If the relative risk for OHIs and CVD was reported in the abstract, then the underlying absolute risks were searched from the full text, and it was assessed whether it was similarly adjusted for confounding than was the relative risk in the abstract.

RESULTS

One hundred-six articles were included. From these, 85 (80%) studies reported the association of OHIs and CVD with one or more relative risks in the abstract. Of those 85 studies, the underlying absolute risks were accessible or calculable from the abstract or full text of 60 studies. However, of these 60 studies, in only 10 (12%), the underlying absolute risks were similarly adjusted, as were the relative risks in the abstract. The absolute risks of CVD by OHIs were rarely reported without corresponding relative risks in the abstract (n = 2, 2%). Median absolute risk difference in the CVD risk between exposure levels to which the first relative risk in the abstract referred was 1.8% (interquartile range 0.6-4.6, n = 63).

CONCLUSIONS

Focusing on relative risks over absolute risks was a common practice in literature. Reporting similarly adjusted underlying absolute risks of relative risks was rare in most studies, despite those being helpful for comprehending the magnitude of CVD-risk increase related to poor oral health. Current reporting practices could lead to an overinterpretation of risk increase of CVD related to poor oral health.

Quantile Treatment Effect of Zinc Lozenges on Common Cold Duration: A Novel Approach to Analyze the Effect of Treatment on Illness Duration

Calculation of the difference of means is the most common approach when analyzing treatment effects on continuous outcomes. Nevertheless, it is possible that the treatment has a different effect on patients who have a lower value of the outcome compared with patients who have a greater value of the outcome. The estimation of quantile treatment effects (QTEs) allows the analysis of treatment effects over the entire distribution of a continuous outcome, such as the duration of illness or the duration of hospital stay. Furthermore, most of these outcomes have asymmetric distributions with fat tails, and censored observations are not uncommon. These features can be accounted for in the analysis of the QTE. In this paper, we use the QTE approach to analyze the effect of zinc lozenges on common cold duration. We use the data set of the Mossad (1996) trial with zinc gluconate lozenges, and three data sets of trials with zinc acetate lozenges. In the Mossad (1996) trial, zinc gluconate lozenges shortened common cold duration on average by 4.0 days (95% CI 2.3-5.7 days). However, the QTE analysis indicates that 15- to 17-day colds were shortened by 8 days, and 2-day colds by just 1 day, for the group taking zinc lozenges. Thus, the overall 4.0-day average effect of zinc gluconate lozenges in the Mossad (1996) trial is inconsistent with our QTE findings for both short and long colds. Similar results were found in our QTE analysis of the pooled data sets of the three zinc acetate lozenge trials. The average effect of 2.7 days (95% CI 1.8-3.3 days) was inconsistent with the effects on short and long colds. The QTE approach may have broad usefulness for examining treatment effects on the duration of illness and hospital stay, and on other similar outcomes.

Discovery solubility measurement and assessment of small molecules with drug development in mind

Solubility is a key physicochemical property for the success of any drug candidate. Although the methods used and their rationales for determining solubility are subject to project needs and stages along the drug discovery-drug development pipeline, an artificial boundary can exist at the discovery-development interface. This boundary results in less effective solubility knowledge sharing and data integration among scientists in both drug discovery and drug development. Herein, we present a refreshed perspective on solubility. Solubility experimentation is not a one-size-fits-all measurement; instead, we stress the importance of constructing a seamless solubility understanding of a molecule as it progresses from a new chemical entity into a drug product.

Composite Bioinks With Mesoporous Bioactive Glasses-A Critical Evaluation of Results Obtained by In Vitro Experiments

Besides osteoconductivity and a high degradation rate, mesoporous bioactive glasses (MBGs) are specific for their highly ordered channel structure and high specific surface area, making them suitable as drug and/or growth factor delivery systems. On the other hand, the mesoporous channel structure and MBG composition can have an effect on common cell evaluation assays, leading to inconclusive results. This effect is especially important when MBG is mixed in composite bioinks, together with cells. Additionally, the hydrogel component of the ink can influence the degradation of MBG, leading to different ion releases, which can additionally affect the analyses. Hence, our aim here was to show how the MBG structure and composition influence common cell viability and differentiation assays when calcium (Ca)- or magnesium (Mg)-containing glass is part of an alginate-based composite bioink. We suggested pre-labeling of cells with DiI prior to bioprinting and staining with calcein-AM to allow identification of metabolically active cells expressing signals in both green and red channels, allowing the use of fluorescence imaging for cell viability evaluations in the presence of high amounts (7 wt %) of MBGs. The release and uptake of ions during degradation of CaMBG and MgMBG were significantly changed by alginate in the composite bioinks, as confirmed by higher release and uptake from bulk glasses. Additionally, we detected a burst release of Mg2+ from composites only after 24 h of incubation. Furthermore, we demonstrated that released ions and the mesoporous channel structure affect the measurement of lactate dehydrogenase (LDH) and alkaline phosphatase activity (ALP) in bioprinted composite scaffolds. Measured LDH activity was significantly decreased in the presence of CaMBG. On the other hand, the presence of MgMBG induced increased signal measured for the ALP. Taken together, our findings show how composite bioinks containing MBGs can interfere with common analyses, obtaining misleading results.

Association of persistent acute kidney injury and renal recovery with mortality in hospitalised patients

Objectives

Acute kidney injury (AKI) affects up to one-quarter of hospitalised patients and 60% of patients in the intensive care unit (ICU). We aim to understand the baseline characteristics of patients who will develop distinct AKI trajectories, determine the impact of persistent AKI and renal non-recovery on clinical outcomes, resource use, and assess the relative importance of AKI severity, duration and recovery on survival.

Methods

In this retrospective, longitudinal cohort study, 156 699 patients admitted to a quaternary care hospital between January 2012 and August 2019 were staged and classified (no AKI, rapidly reversed AKI, persistent AKI with and without renal recovery). Clinical outcomes, resource use and short-term and long-term survival adjusting for AKI severity were compared among AKI trajectories in all cohort and subcohorts with and without ICU admission.

Results

Fifty-eight per cent (31 500/54 212) had AKI that rapidly reversed within 48 hours; among patients with persistent AKI, two-thirds (14 122/22 712) did not have renal recovery by discharge. One-year mortality was significantly higher among patients with persistent AKI (35%, 7856/22 712) than patients with rapidly reversed AKI (15%, 4714/31 500) and no AKI (7%, 22 117/301 466). Persistent AKI without renal recovery was associated with approximately fivefold increased hazard rates compared with no AKI in all cohort and ICU and non-ICU subcohorts, independent of AKI severity.

Discussion

Among hospitalised, ICU and non-ICU patients, persistent AKI and the absence of renal recovery are associated with reduced long-term survival, independent of AKI severity.

Conclusions

It is essential to identify patients at risk of developing persistent AKI and no renal recovery to guide treatment-related decisions.

Driving Single Cell Proteomics Forward with Innovation

Current single-cell mass spectrometry (MS) methods can quantify thousands of peptides per single cell while detecting peptide-like features that may support the quantification of 10-fold more peptides. This 10-fold gain might be attained by innovations in data acquisition and interpretation even while using existing instrumentation. This perspective discusses possible directions for such innovations with the aim to stimulate community efforts for increasing the coverage and quantitative accuracy of single proteomics while simultaneously decreasing missing data. Parallel improvements in instrumentation, sample preparation, and peptide separation will afford additional gains. Together, these synergistic routes for innovation project a rapid growth in the capabilities of MS based single-cell protein analysis. These gains will directly empower applications of single-cell proteomics to biomedical research.

Two-Step Approach to Processing Raw Strain Monitoring Data for Damage Detection of Structures under Operational Conditions

Strain data of structural health monitoring is a prospective to be made full use of, because it reflects the stress peak and fatigue, especially sensitive to local stress redistribution, which is the probably damage in the vicinity of the sensor. For decoupling structural damage and masking effects caused by operational conditions to eliminate the adverse impacts on strain-based damage detection, small time-scale structural events, i.e., the short-term dynamic strain responses, are analyzed in this paper by employing unsupervised modeling. A two-step approach to successively processing the raw strain monitoring data in the sliding time window is presented, consisting of the wavelet-based initial feature extraction step and the decoupling step to draw damage indicators. The principal component analysis and a low-rank property-based subspace projection method are adopted as two alternative decoupling methodologies. The approach’s feasibility and robustness are substantiated by analyzing the strain monitoring data from a customized truss experiment to successfully remove the masking effects of operating loads and identify local damages even concerning accommodating situations of missing data and limited measuring points. This work also sheds light on the merit of a low-rank property to separate structural damages from masking effects by comparing the performances of the two optional decoupling methods of the distinct rationales.

Pitfalls and perils of survival analysis under incorrect assumptions: the case of COVID-19 data

Non-parametric survival analysis has become a very popular statistical method in current medical research. However, resorting to survival analysis when its fundamental assumptions are not fulfilled can severely bias the results. Currently, hundreds of clinical studies are using survival methods to investigate factors potentially associated with the prognosis of coronavirus disease 2019 (COVID-19) and test new preventive and therapeutic strategies. In the pandemic era, it is more critical than ever to base decision-making on evidence and rely on solid statistical methods, but this is not always the case. Serious methodological errors have been identified in recent seminal studies about COVID-19: One reporting outcomes of patients treated with remdesivir and another one on the epidemiology, clinical course, and outcomes of critically ill patients. High-quality evidence is essential to inform clinicians about optimal COVID-19 therapies and policymakers about the true effect of preventive measures aiming to tackle the pandemic. Though timely evidence is needed, we should encourage the appropriate application of survival analysis methods and careful peer-review to avoid publishing flawed results, which could affect decision-making. In this paper, we recapitulate the basic assumptions underlying non-parametric survival analysis and frequent errors in its application and discuss how to handle data on COVID-19.

Practical Considerations for Initiating and Utilizing Flash Continuous Glucose Monitoring in Clinical Practice

Use of continuous glucose monitoring (CGM) has been shown to improve clinical outcomes in patients with type 1 diabetes (T1D) and type 2 diabetes (T2D), including improved glycemic control, better treatment adherence, and an increased understanding of their treatment regimens. Retrospective analysis of CGM data allows clinicians and patients to identify glycemic patterns that support and facilitate informed therapy adjustments. There are currently 2 types of CGM systems: real-time CGM (rtCGM) and flash CGM. The FreeStyle Libre 2 (FSL2) is the newest flash CGM system commercially available. Because the FSL2 system was only recently cleared for use in the US, many endocrinologists and diabetes specialists may be unfamiliar with the strengths, limitations, and potential of the FSL2 system. This article focuses on practical approaches and strategies for initiating and using flash CGM in endocrinology and diabetes specialty practices.

Interpretation of melperone intoxication: post-mortem concentration distribution and interpretation of intoxication data

OBJECTIVES

Since melperone abuse with lethal intoxication is common, expert opinions based on therapeutical and lethal concentration ranges can be considered as important. Because there is a lack of information about fatalities caused by melperone mono-intoxications and data on tissue samples with concentration distribution, the aim of this work is the examination of lethal concentration ranges of melperone and drug quantification in different matrices.

METHODS

An LC-MS/MS method was applied for analyses performed in blood and tissue samples. Quantification based on standard addition and sample preparation on liquid-liquid extraction with 1-chlorobutane. An appropriate tissue homogenization was performed ahead of extraction with an IKA Ultra-Turrax-Tube-Drive^®. A Luna 5 µm C18 (2) 100 Å, 150  × 2 mm analytical column was used for chromatographic separation and the elution was performed with two mobile phases consisted of A (H₂O/methanol = 95/5, v/v) and B (H₂O/methanol = 3/97, v/v) both with 10 mM ammonium acetate and 0.1% acetic acid.

RESULTS

A multi-drug LC-MS/MS analytical method developed was applied successfully for melperone quantification in different post-mortem matrices. No analytical problems could be identified during method development and analyses of real samples. The melperone lethal concentration calculated in femoral blood of the drug mono-intoxication investigated was 10 mg/L. Melperone concentration distribution was presented for the first time.

CONCLUSIONS

The lethal reference concentration of melperone in femoral blood of 17.1 mg/L pointed out in different reference lists should be used with caution. Instead, a lower lethal melperone concentration should be considered. The post-mortem concentration distribution of the drug presented could be helpful in the interpretation of cases where no blood samples are available.

Statistical inference in abstracts of three influential clinical pharmacology journals analyzed using a text-mining algorithm

AIM

To describe the trend in the prevalence of statistical inference in three influential clinical pharmacology journals METHODS: We applied a computer-based algorithm to abstracts of three clinical pharmacology journals published in 1976 to 2016 to identify statistical inference and its subtypes. Furthermore, we manually reviewed a random sample of 300 articles to access algorithm's performance in finding statistical inference in abstracts and as a screening tool for presence and absence of statistical inference in full text.

RESULT

The algorithm identified 59% (13,375/22,516 [mid p 95% CI, 59%-60%]) article abstracts with statistical inference. The percentage of abstracts with statistical inference was similar in 1976 and 2016, 48% (179/377 [mid p 95%CI, 42%-52%]) versus 49% (386/791 [mid p 95%CI, 45%-52%]). Statistical reporting pattern varied among journals. Among abstracts containing any statistical inference in the publications from 1976 to 2016 null-hypothesis significance testing was the most prevalent reported statistical inference. The algorithm had high sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for finding statistical inferences in abstract. While PPV for predicting the statistical inference in full text (including abstract, text, tables and figures) was high, NPV was low.

CONCLUSION

Despite journal's editorials and statistical associations' guidelines, most authors focused on testing rather than estimation. In future, a better statistical reporting might be ensured by improving the statistical knowledge of authors and an addition of statistical guides to journals' instruction to authors to the extent that editors would like their statistical inference preferences to be incorporated into submitted manuscripts.

Interpretation of melperone intoxication: post-mortem concentration distribution and interpretation of intoxication data

OBJECTIVES

Since melperone abuse with lethal intoxication is common, expert opinions based on therapeutical and lethal concentration ranges can be considered as important. Because there is a lack of information about fatalities caused by melperone mono-intoxications and data on tissue samples with concentration distribution, the aim of this work is the examination of lethal concentration ranges of melperone and drug quantification in different matrices.

METHODS

An LC-MS/MS method was applied for analyses performed in blood and tissue samples. Quantification based on standard addition and sample preparation on liquid-liquid extraction with 1-chlorobutane. An appropriate tissue homogenization was performed ahead of extraction with an IKA Ultra-Turrax-Tube-Drive®. A Luna 5 µm C18 (2) 100 Å, 150  × 2 mm analytical column was used for chromatographic separation and the elution was performed with two mobile phases consisted of A (H2O/methanol = 95/5, v/v) and B (H2O/methanol = 3/97, v/v) both with 10 mM ammonium acetate and 0.1% acetic acid.

RESULTS

A multi-drug LC-MS/MS analytical method developed was applied successfully for melperone quantification in different post-mortem matrices. No analytical problems could be identified during method development and analyses of real samples. The melperone lethal concentration calculated in femoral blood of the drug mono-intoxication investigated was 10 mg/L. Melperone concentration distribution was presented for the first time.

CONCLUSIONS

The lethal reference concentration of melperone in femoral blood of 17.1 mg/L pointed out in different reference lists should be used with caution. Instead, a lower lethal melperone concentration should be considered. The post-mortem concentration distribution of the drug presented could be helpful in the interpretation of cases where no blood samples are available.

LinX: A Software Tool for Uncommon Cross-Linking Chemistry

Chemical cross-linking mass spectrometry has become a popular tool in structural biology. Although several algorithms exist that efficiently analyze data-dependent mass spectrometric data, the algorithm to identify and quantify intermolecular cross-links located at the interaction interface of homodimer molecules was missing. The algorithm in LinX utilizes high mass accuracy for ion identification. In contrast with standard data-dependent analysis, LinX enables the elucidation of cross-linked peptides originating from the interaction interface of homodimers labeled by ¹⁴N/¹⁵N, including their ratio or cross-links from protein-nucleic acid complexes. The software is written in Java language, and its source code and a detailed user's guide are freely available at https://github.com/KukackaZ/LinX or https://ms-utils.org/LinX. Data are accessible via the ProteomeXchange server with the data set identifier PXD023522.

Interpreting Bibliometric Data

Many academic analyses of good practice in the use of bibliometric data address only technical aspects and fail to account for and appreciate user requirements, expectations, and actual practice. Bibliometric indicators are rarely the only evidence put before any user group. In the present state of knowledge, it is more important to consider how quantitative evaluation can be made simple, transparent, and readily understood than it is to focus unduly on precision, accuracy, or scholarly notions of purity. We discuss how the interpretation of 'performance' from a presentation using accurate but summary bibliometrics can change when iterative deconstruction and visualization of the same dataset is applied. From the perspective of a research manager with limited resources, investment decisions can easily go awry at governmental, funding program, and institutional levels. By exploring select real-life data samples we also show how the specific composition of each dataset can influence interpretive outcomes.

Evaluation of interpretation methods to improve accuracy of the prenatal BACs-on-Beads™ assay in prenatal diagnosis

Prenatal BACs-on-Beads™ (PNBoBs™) technology has been approved for use in routine clinical prenatal diagnosis in numerous countries. However, the influence of data interpretation on the accuracy of the results remains to be evaluated. The present study aimed to determine the accuracy of existing data interpretation approaches and develop an optimization method to improve the performance of the PNBoBs™ assay in prenatal diagnosis. A total of 2,289 prenatal cases with known karyotypes and raw ratio data from PNBoBs™ assays were recruited for the present study. Positive results, according to the data interpretation methods used for the PNBoBs™ test, were validated against current gold-standard approaches. Statistical analyses were then performed to evaluate the accuracy of existing methods in data interpretation to provide a basis for the optimization of a follow-up approach. Among the existing methods, the ‘trimmed standard deviation threshold’ approach had the highest sensitivity and false-positive rates, with 98.1 and 4.2%, respectively. The ‘n-1 or greater probes’ rule had the highest specificity (99.7%) and the second-highest false-negative rate (11.5%). The method optimized in the present study provided a reasonable balance between sensitivity (98.1%) and specificity (99.6%) with regards to the interpretation of the data obtained from the PNBoBs™ assay. The results indicated that the present optimization method outperforms existing approaches in data interpretation for the PNBoBs™ assay, and as a result, may reduce unnecessary verification turnaround time and cost in prenatal diagnosis.

Structure of communities in semantic networks of biomedical research on disparities in health and sexism

Introducción.

Como una iniciativa para mejorar la calidad de la atención sanitaria, en la investigación biomédica se ha incrementado la tendencia centrada en el estudio de las disparidades en salud y sexismo.

Objetivo.

Caracterizar la evidencia científica sobre la disparidad en salud definida como la brecha existente entre la distribución de la salud y el posible sesgo por sexo en el acceso a los servicios médicos.

Materiales y métodos.

Se hizo una búsqueda simultánea de la literatura científica en la base de datos Medline PubMed de dos descriptores fundamentales: Healthcare disparities y Sexism. Posteriormente, se construyó una red semántica principal y se determinaron algunas subunidades estructurales (comunidades) para el análisis de los patrones de organización de la información. Se utilizó el programa de código abierto Cytoscape para el analisis y la visualización de las redes y el MapEquation, para la detección de comunidades. Asimismo, se desarrolló código ex profeso disponible en un repositorio de acceso público.

Resultados.

El corpus de la red principal mostró que los términos sobre las enfermedades del corazón fueron los descriptores de condiciones médicas más concurrentes. A partir de las subunidades estructurales, se determinaron los patrones de información relacionada con las políticas públicas, los servicios de salud, los factores sociales determinantes y los factores de riesgo, pero con cierta tendencia a mantenerse indirectamente conectados con los nodos relacionados con condiciones médicas.

Conclusiones.

La evidencia científica indica que la disparidad por sexo sí importa para la calidad de la atención de muchas enfermedades, especialmente aquellas relacionadas con el sistema circulatorio. Sin embargo, aún se percibe un distanciamiento entre los factores médicos y los sociales que dan lugar a las posibles disparidades por sexo.

Cardiology Fellow Diagnostic Accuracy and Data Interpretation Outcomes: A Review of the Current Literature

Background

Cardiology fellows, in particular, are in a unique position to mold the new cardiovascular workforce, especially in terms of risk prevention. There is a growing need for the cardiovascular workforce. In the United States, one person dies every forty-two seconds due to a cardiovascular adverse event.

Methods

A PRISMA systematic review included comprehensive search of the MED-LINE database (PubMed) from 1927 to 2020 – the oldest to newest available literature on the subject available through PubMed.

Results

Fifty-seven cardiology fellows together interpreted a total of 1719 EKGs with a correct rate of 52%. Sixty-four fellows completed a total of 1363 echocardiography interpretations with an accuracy rate of nearly 75%.

Conclusion

Based on the studies discussed, it is evident that a cardiology fellow, particularly in their early years of training, may be limited due to a lack of experience. With continued EKG and echocardiogram interpretation, as well as other clinical skills practice, fellows can improve their diagnostic accuracy and procedural efficiency.

Application of automated nonparametric statistical analysis in clinical contexts

Functional analyses (FAs) provide clinicians with results upon which they design behavioral treatments. Unfortunately, interrater reliability of visual analysis of FA results can be inconsistent. Accordingly, researchers have designed quantitative metrics and visual aids to supplement visual analysis. Recently, Hall et al. (2020) provided a proof of concept for using automated nonparametric statistical analysis (ANSA) to interpret FA data. Their results show promise for ANSA as a supplemental tool. However, they evaluated ANSA with only published FA datasets, which may not be representative of FAs commonly encountered in clinical care. Therefore, the purpose of this replication was to compare ANSA to another validated supplemental aid (i.e., the structured criteria method) and investigate its utility with unpublished clinical FA data. Our results were consistent with Hall et al.'s, indicating ANSA may augment clinical interpretation of FA data. Recommendations for clinical applications of ANSA and future directions for researchers are discussed.

Reliable measurements of extracellular vesicles by clinical flow cytometry

Extracellular vesicles (EVs) are cell‐derived particles with a phospholipid membrane present in all body fluids. Because EV properties change in health and disease, EVs have excellent potential to become biomarkers for diagnosis, prognosis, or monitoring of disease. The only technique capable of detecting, sizing, and phenotyping a million of EVs within minutes is (clinical) flow cytometry. A flow cytometer measures light scattering and fluorescence signals of single EVs. Although these signals contain valuable information about the presence and composition of EVs, the signals are expressed in arbitrary units, which make the comparison of measurement results impossible between instruments and laboratories. Additionally, unintended and undocumented variations in the source, preparation, and analysis of the sample lead to orders of magnitude variations in the measured EV concentrations. Here, we will explain the basics, challenges, and common misconceptions of EV flow cytometry. In addition, we provide an overview of recent standardization initiatives, which are a prerequisite for comparison of clinical data and thus for clinical biomarker exploration of EVs.

A Review of Measurement Calibration and Interpretation for Seepage Monitoring by Optical Fiber Distributed Temperature Sensors

Seepage flow through embankment dams and their sub-base is a crucial safety concern that can initiate internal erosion of the structure. The thermometric method of seepage monitoring employs the study of heat transfer characteristics in the soils, as the temperature distribution in earth-filled structures can be influenced by the presence of seepage. Thus, continuous temperature measurements can allow detection of seepage flows. With the recent advances in optical fiber temperature sensor technology, accurate and fast temperature measurements, with relatively high spatial resolution, have been made possible using optical fiber distributed temperature sensors (DTSs). As with any sensor system, to obtain a precise temperature, the DTS measurements need to be calibrated. DTS systems automatically calibrate the measurements using an internal thermometer and reference section. Additionally, manual calibration techniques have been developed which are discussed in this paper. The temperature data do not provide any direct information about the seepage, and this requires further processing and analysis. Several methods have been developed to interpret the temperature data for the localization of the seepage and in some cases to estimate the seepage quantity. An efficient DTS application in seepage monitoring strongly depends on the following factors: installation approach, calibration technique, along with temperature data interpretation and post-processing. This paper reviews the different techniques for calibration of DTS measurements as well as the methods of interpretation of the temperature data.

Checklist for clinical applicability of subgroup analysis

WHAT IS KNOWN AND OBJECTIVE

Subgroup analysis plays an important role in clinical decision-making. Correct management of subgroup analysis is necessary to optimize effectiveness, safety and efficiency of treatments. No homogeneous criteria have been developed for interpretation of subgroup analysis. In this study, the researcher develops a checklist to evaluate the reliability and applicability of the results of subset analyses.

METHODS

With a review of previous literature, three main criteria were included in the checklist: statistical association, biological plausibility and consistency among studies. Statistical association considered interaction probability, prespecification of analysis, number of subgroups analysed, sample size and positive/negative result in global analysis. Each item was given an indicative score. Total score was related to a level of applicability for the results in clinical practice. Checklist validation included interinvestigator concordance and assessment about utility. Three drug examples were used to validate the tool.

RESULTS AND DISCUSSION

Twenty-six evaluators showed adequate interinvestigator concordance (kappa 0.79, 1 and 0.83 for each drug example regarding applicability). Kappa values increased to 0.94, 1 and 1 after group discussion. Checklist utility score was greater than 4.7/5 in three drug examples. In pre-analysis, inter-researcher agreement on global applicability recommendation of subgroup results to practice was 92.3% (ramucirumab), 96% (nivolumab) and 100% (mepolizumab). In post-analysis, inter-researcher agreement on applicability recommendation of subgroup results was 100%, 94.45% and 100%, respectively. The checklist validation shows a high interindividual agreement of the results, both with respect to the evaluation of the applicability of subgroup analysis and concerning clinical decision-making.

WHAT IS NEW AND CONCLUSION

We have developed the first validated tool for interpretation of subgroup analyses. The checklist contributes to the adoption of homogeneous criteria for subgroup analyses, thereby allowing discussion and evaluation of the effects of a health intervention.

Variability associated with interpreting drugs within forensic hair analysis: A three-stage interpretation

Hair analysis is capable of determining both an individual's long-term drug history and a single exposure to a drug, which can be particularly important for corroborating incidents of drug-facilitated crimes. As a source of forensic evidence that may be used in a court of law, it must be credible, impartial and reliable, yet the pathways of drug and metabolite entry into hair are still uncertain. Many variables may influence drug analysis results, most of which are outside of the control of an analyst. An individual's pharmacokinetic and metabolic responses, hair growth rates, drug incorporation routes, axial migration, ethnicity, age and gender, for example, all display interpersonal variability. At present there is little standardization of the analytical processes involved with hair analysis. Both false positives and negative results for drugs are frequently encountered, regardless of whether a person has consumed a drug or not. In this regard, we have categorized these variables and proposed a three-stage analytical approach to facilitate forensic toxicologists, hair analysis experts, judiciaries and service users in the analytical and interpretation process.

Comparing the Efficacy of Static and Dynamic Graph Types in Communicating Complex Statistical Relationships

Graphs are useful tools to communicate meaningful patterns in data, but their efficacy varies considerably based on the figure's construction and presentation medium. Specifically, a digital format figure can be dynamic, allowing the reader to manipulate it and little is known about the efficacy of dynamic figures. This present study compared how effectively static and dynamic graphical formats convey relationship information, and in particular variable interactions. Undergraduates (N = 128, 56% female, M _age = 18.9) were given a brief tutorial on main effects and interactions in data and then answered 48 multiple-choice questions about specific graphs. Each question involved one of four figure types and one of four relationship types (main effect only, interaction only, main effect and interaction, or no relationship), with relationship types and graphical formats fully crossed. Multilevel logistic regression analysis revealed that participants were fairly accurate at detecting main effects and null relationships but struggled with interaction effects. Additionally, the static 3D graph lowered performance for detecting main effects, although this negative effect disappeared when participants were allowed to rotate the 3D graph. These results suggest that dynamic figures in digital publications are a potential tool to effectively communicate data, but they are not a panacea. Undergraduates continued to struggle with more complicated relationships (e.g., interactions) regardless of graph type. Future studies will need to examine more experienced populations and additional dynamic graph formats, especially ones tailored for demonstrating interactions (e.g., profiler plots).

Eight in Every 10 Abstracts of Low Back Pain Systematic Reviews Presented Spin and Inconsistencies With the Full Text: An Analysis of 66 Systematic Reviews

STUDY DESIGN

Overview study.

BACKGROUND

Abstracts of systematic reviews have presented 'spin' (i.e. overstated interpretation of study results) and inconsistency with the full text.

OBJECTIVES

1. Do abstracts of low back pain reviews contain spin? 2. Do these abstracts consistently represent the full text? 3. Is abstract spin associated with the type of conclusion?

METHODS

We searched the Physiotherapy Evidence Database (PEDro) on 10th January 2018. Data were extracted from systematic reviews of physiotherapy interventions for low back pain, published between 2015 and 2017. Spin was assessed using a 7-item checklist. We evaluated consistency by comparing information contained in the abstract and the full text using the 7-item checklist with Kappa coefficient analysis. We used logistic regression analysis to evaluate the association between spin in the abstract and type of conclusion. We evaluated methodological quality using the AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews).

RESULTS

We included 66 eligible systematic reviews, subdivided into Cochrane (n=9) and non-Cochrane (n=57) reviews. There was some form of spin in 80% of abstracts. Abstracts of non-Cochrane reviews were not consistent with the full text (fair to moderate agreement). Cochrane review abstracts had substantial to almost perfect agreement with the full text. Spin was not associated with the type of conclusion in all systematic reviews (P < 0.05). The methodological quality ranged from 'high' to 'critically low'.

CONCLUSIONS

The abstracts of systematic reviews evaluating physiotherapy interventions for low back pain need improvement. J Orthop Sports Phys Ther, Epub 23 Aug 2019. doi:10.2519/jospt.2020.8962.

Statistical Accuracy in Rheumatology Research

Science and related research activities are always subject to errors. Statistical accuracy is necessary in order to overcome erroneous researching and reporting practices. The present article aimed to review the current literature on statistical errors in clinical medicine articles, and to provide rheumatologists with basic recommendations regarding the use of common statistical methods in research articles. With this purpose, PubMed/MEDLINE and Web of Science databases were searched by using relevant keywords. Data so far indicate that statistical errors are common in published articles from several disciplines of medicine. Statistics is the key element of any research activity, thus, implementing statistics at each step (hypothesis development, study design, sampling/data collection, data analysis, presentation) of every research is mandatory. In this regard, awareness of common statistical errors, basic knowledge on statistical methodology and consulting an expert in biostatistics from the beginning of the research process would be of value for rheumatologists.

Molstack: A platform for interactive presentations of electron density and cryo-EM maps and their interpretations

In the Special Issue on Tools for Protein Science in 2018, we presented Molstack: a concept of a cloud-based platform for sharing electron density maps and their interpretations. Molstack is a web platform that allows the interactive visualization of density maps through the simultaneous presentation of multiple datasets and models in a way that allows for easy pairwise comparison. We anticipated that the users of this conceptually simple platform would find many different uses for their projects, and we were not mistaken. We have observed researchers use Molstack to present experimental evidence for their models in the form of electron density maps, omit maps, and anomalous difference density maps. Users also employed Molstack to present alternative interpretations of densities, including rerefinements and speculative interpretations. While we anticipated these types of projects to be the main use cases, we were pleased to see Molstack used to display superpositions of different models, as a tool for story-driven presentations, and for collaboration as well. Here, we present developments in the platform that were driven by user feedback, highlight several cases that used Molstack to enhance the publication, and discuss possible directions for the platform.

Collaborative Workshops for Community Meaning-Making and Data Analyses: How Focus Groups Strengthen Data by Enhancing Understanding and Promoting Use

Community-based participatory research is a growing approach, but often includes higher levels of community engagement in the research design and data collection stages than in the data interpretation stage. Involving study participants in this stage could further knowledge justice, science that aligns with and supports social justice agendas. This article reports on two community-based participatory environmental health surveys conducted between 2015 and 2019 in an industrial region near Marseille, France, and focuses specifically on our approach of organizing focus groups to directly involve residents and community stakeholders in the analysis and interpretation process. We found that, in these focus groups, residents triangulated across many different sources of information-study findings, local knowledge, and different types of expert knowledge-to reach conclusions about the health of their community and make recommendations for what should be done to improve community health outcomes. We conclude that involving residents in the data analysis and interpretation stage can promote epistemic justice and lead to final reports that are more useful to community stakeholders and decision-makers.

Time-Series-Based Leakage Detection Using Multiple Pressure Sensors in Water Distribution Systems

Leak detection is nowadays an important task for water utilities as leakages in water distribution systems (WDS) increase economic costs significantly and create water resource shortages. Monitoring data such as pressure and flow rate of WDS fluctuate with time. Diagnosis based on time series monitoring data is thought to be more convincing than one-time point data. In this paper, a threshold selection method for the correlation coefficient based on time series data is proposed based on leak scenario falsification, to explore the advantages of data interpretation based on time series for leak detection. The approach utilizes temporal varying correlation between data from multiple pressure sensors, updates the threshold values over time, and scans multiple times for a scanning time window. The effect of scanning time window length on threshold selection is also tested. The performance of the proposed method is tested on a real, full-scale water distribution network using synthetic data, considering the uncertainty of demand and leak flow rates, sensor noise, and so forth. The case study shows that the scanning time window length of 3-6 achieves better performance; the potential of the method for leak detection performance improvement is confirmed, though affected by many factors such as modeling and measurement uncertainties.

Data Work: Meaning-Making in the Era of Data-Rich Medicine

In the era of data-rich medicine, an increasing number of domains of people’s lives are datafied and rendered usable for health care purposes. Yet, deriving insights for clinical practice and individual life choices and deciding what data or information should be used for this purpose pose difficult challenges that require tremendous time, resources, and skill. Thus, big data not only promises new clinical insights but also generates new—and heretofore largely unarticulated—forms of work for patients, families, and health care providers alike. Building on science studies, medical informatics, Anselm Strauss and colleagues’ concept of patient work, and subsequent elaborations of articulation work, in this article, we analyze the forms of work engendered by the need to make data and information actionable for the treatment decisions and lives of individual patients. We outline three areas of data work, which we characterize as the work of supporting digital data practices, the work of interpretation and contextualization, and the work of inclusion and interaction. This is a first step toward naming and making visible these forms of work in order that they can be adequately seen, rewarded, and assessed in the future. We argue that making data work visible is also necessary to ensure that the insights of big and diverse datasets can be applied in meaningful and equitable ways for better health care.

Increased Reporting of Exclusionary Diagnoses Inflate Apparent Reductions in Long-Stay Antipsychotic Prescribing

OBJECTIVE

Over the two years following the 2012 introduction of CMS's National Partnership, combined rates of schizophrenia, Tourette's, and Huntington's in US long-stay residents increased 12%. We evaluated trends in reporting of these diagnoses for the subgroup of long-stay residents on antipsychotics.

METHODS

Retrospective analysis of Virginia Medicaid claims identified annual utilization rates of psychiatric diagnoses for long-stay seniors on antipsychotics. Chi-square analysis compared rates for the year before March, 2012 with the same 12-month period 1 year later. A 5-year pre-existing baseline rate was also obtained.

RESULTS

Diagnosis rates for 2011 were unchanged from baseline. Comparing 2011 with 2013, diagnoses rates for schizophrenia, Tourette's, and Huntington's combined increased 40% (p < .0001), primarily because schizophrenia reporting nearly doubled (p < .0001).

CONCLUSIONS

For long-stay seniors on antipsychotics, reporting of schizophrenia, Tourette's, and Huntington's began increasing in 2012 and at almost triple the rate CMS described for the general long-stay population. The increased reporting of these diagnoses described by CMS since 2012 appears to be new and concentrated in residents on antipsychotics Clinical Implications: Since antipsychotics prescribed for schizophrenia, Tourette's, and Huntington's are excluded from quality-measure auditing, apparent reductions in inappropriate long-stay antipsychotic use since the National Partnership may be exaggerated.

Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer Forum

Background

The increasing use of social media and mHealth apps has generated new opportunities for health care consumers to share information about their health and well-being. Information shared through social media contains not only medical information but also valuable information about how the survivors manage disease and recovery in the context of daily life.

Objective

The objective of this study was to determine the feasibility of acquiring and modeling the topics of a major online breast cancer support forum. Breast cancer patient support forums were selected to discover the hidden, less obvious aspects of disease management and recovery.

Methods

First, manual topic categorization was performed using qualitative content analysis (QCA) of each individual forum board. Second, we requested permission from the Breastcancer.org Community for a more in-depth analysis of the postings. Topic modeling was then performed using open source software Machine Learning Language Toolkit, followed by multiple linear regression (MLR) analysis to detect highly correlated topics among the different website forums.

Results

QCA of the forums resulted in 20 categories of user discussion. The final topic model organized >4 million postings into 30 manageable topics. Using qualitative analysis of the topic models and statistical analysis, we grouped these 30 topics into 4 distinct clusters with similarity scores of ≥0.80; these clusters were labeled Symptoms & Diagnosis, Treatment, Financial, and Family & Friends. A clinician review confirmed the clinical significance of the topic clusters, allowing for future detection of actionable items within social media postings. To identify the most significant topics across individual forums, MLR demonstrated that 6 topics—based on the Akaike information criterion values ranging from −642.75 to −412.32—were statistically significant.

Conclusions

The developed method provides an insight into the areas of interest and concern, including those not ascertainable in the clinic. Such topics included support from lay and professional caregivers and late side effects of therapy that consumers discuss in social media and may be of interest to clinicians. The developed methods and results indicate the potential of social media to inform the clinical workflow with regards to the impact of recovery on daily life.

Differential Identification of Three Young Housefire Victims: Methods when Age Assessment Fails

Multiple fatality incidents involving more than one child of statistically same age (including twins) can be challenging from an identification standpoint. This case details an urban fire, in which four children perished. Age assessment on three of the victims utilizing maturity staging described by Moorrees, Fanning, and Hunt yielded insignificant results. However, a plot of the MFH data shows the difference between two identical twins and a third child. The twins share a similar growth pattern, whereas the other was different. Based on this graphical interpretation, the nontwin victim was positively identified through exclusion. These results were verified through statistical testing. This case demonstrates a method to repurpose age assessment data to graphically distinguish between child victims. Further, it is shown that radiographic and clinical presentation in childhood identical twins can elicit genetic versus acquired similarities and differences, which can be used for identification of individuals and exclusion of others.

The Empirical Fluctuation Pattern of E. coli Division Control

In physics, it is customary to represent the fluctuations of a stochastic system at steady state in terms of linear response to small random perturbations. Previous work has shown that the same framework describes effectively the trade-off between cell-to-cell variability and correction in the control of cell division of single E. coli cells. However, previous analyses were motivated by specific models and limited to a subset of the measured variables. For example, most analyses neglected the role of growth rate variability. Here, we take a comprehensive approach and consider several sets of available data from both microcolonies and microfluidic devices in different growth conditions. We evaluate all the coupling coefficients between the three main measured variables (interdivision times, cell sizes and individual-cell growth rates). The linear-response framework correctly predicts consistency relations between a priori independent experimental measurements, which confirms its validity. Additionally, the couplings between the cell-specific growth rate and the other variables are typically non zero. Finally, we use the framework to detect signatures of mechanisms in experimental data involving growth rate fluctuations, finding that (1) noise-generating coupling between size and growth rate is a consequence of inter-generation growth rate correlations and (2) the correlation patterns agree with a near-adder model where the added size has a dependence on the single-cell growth rate. Our findings define relevant constraints that any theoretical description should reproduce, and will help future studies aiming to falsify some of the competing models of the cell cycle existing today in the literature.

Potential patient harms from misinterpretation of publically reported surgical outcomes

OBJECTIVE

To determine how the general public interprets surgical complication rates presented from a publicly available online surgical-rating website.

SUBJECTS AND METHODS

An in-person electronic survey was administered at the local State Fair to a convenience sample. Participants were presented with a representative output from an online surgeon-rating website and were asked to choose from three statistically equivalent surgeons for a hypothetical medical decision. We then suggested that their insurance company would only cover one surgeon and probed their willingness to pay to switch surgeons for a small chance of lowering the risk of a complication (0.7%, 95% confidence interval [CI] -8.1% to 9.5%, P = 0.9). We quantified the characteristics of those willing to switch, the degree of misinterpretation, and the subsequent potential patient harms.

RESULTS

There were 343 completed responses. When presented with a hypothetical healthcare decision, most participants (n = 209, 61%) said they were willing to pay out-of-pocket expenses to switch to a statistically equivalent surgeon. Those who were willing to pay to switch surgeons were more likely to be older (odds ratio [OR] 1.02, 95% CI 1.01-1.03), poorer (OR 1.81, 95% CI 1.07-3.11), previously had cancer (OR 5.9, 95% CI 1.9-25), and misinterpreted the data (OR 3.03, 95% CI 1.87-4.96). Those who were willing to pay out-of-pocket expenses were more inaccurate in their estimation of surgeon complication rates (mean estimate 34.0% vs 8.9%, P < 0.001, correct rate = 3.6%), and on average were willing to pay $6 494 (95% CI 4 108-8 880).

CONCLUSION

Understanding of a publicly reported surgical-complication website is often prone to misinterpretation by the general population and may lead to patient harm from a financial aspect.

Supporting successful interpretations of covariation data: Beneficial effects of variable symmetry and problem context

People often have difficulty interpreting covariation data presented in contingency tables. The present study investigates adults' success and strategy use in interpreting covariation data as a function of two factors that may influence performance: symmetry and context. We hypothesised that symmetrical problems, which involve comparing two candidate causes, would elicit more adequate interpretations than asymmetrical problems, which involve comparing the presence and absence of one candidate cause. We also hypothesised that problems with grounded, concrete contexts would elicit more adequate interpretations than abstract problems. College students ( N = 109) interpreted contingency tables in four conditions that varied in symmetry and context. Both factors influenced correct interpretations. A latent class analysis revealed three distinct strategies: (a) compute conditional probabilities (40%), (b) compare-two cells (23%), and (c) anchor and compare (37%), a novel strategy in which reasoners integrate data from all four cells in a stimulus-driven but incorrect way. Participants' use of the conditional-probabilities strategy was higher in the symmetrical than the asymmetrical conditions, and higher in the concrete than the abstract conditions. Thus, even in a sample of college students, interpreting covariation data is a skill that is not fully consolidated, but instead depends on problem structure and context.

Transcriptome profiling of sorted endoreduplicated nuclei from tomato fruits: how the global shift in expression ascribed to DNA ploidy influences RNA-Seq data normalization and interpretation

As part of normal development most eukaryotic organisms, ranging from insects and mammals to plants, display variations in nuclear ploidy levels resulting from somatic endopolyploidy. Endoreduplication is the major source of endopolyploidy in higher plants. Endoreduplication is a remarkable characteristic of the fleshy pericarp tissue of developing tomato fruits, where it establishes a highly integrated cellular system that acts as a morphogenetic factor supporting cell growth. However, the functional significance of endoreduplication is not fully understood. Although endoreduplication is thought to increase metabolic activity due to a global increase in transcription, the issue of gene-specific ploidy-regulated transcription remains open. To investigate the influence of endoreduplication on transcription in tomato fruit, we tested the feasibility of a RNA sequencing (RNA-Seq) approach using total nuclear RNA extracted from purified populations of flow cytometry-sorted nuclei based on their DNA content. Here we show that cell-based approaches to the study of RNA-Seq profiles need to take into account the putative global shift in expression between samples for correct analysis and interpretation of the data. From ploidy-specific expression profiles we found that the activity of cells inside the pericarp is related both to the ploidy level and their tissue location.