MEDLINE search retrieval issues: A longitudinal query analysis of five vendor platforms

Title, abstract and keywords: a practical guide to maximize the visibility and impact of academic papers

In a growing digital landscape, enhancing the discoverability and resonance of scientific articles is essential. Here, we offer 10 recommendations to amplify the discoverability of studies in search engines and databases. Particularly, we argue that the strategic use and placement of key terms in the title, abstract and keyword sections can boost indexing and appeal. By surveying 230 journals in ecology and evolutionary biology, we found that current author guidelines may unintentionally limit article findability. Our survey of 5323 studies revealed that authors frequently exhaust abstract word limits-particularly those capped under 250 words. This suggests that current guidelines may be overly restrictive and not optimized to increase the dissemination and discoverability of digital publications. Additionally, 92% of studies used redundant keywords in the title or abstract, undermining optimal indexing in databases. We encourage adopting structured abstracts to maximize the incorporation of key terms in titles, abstracts and keywords. In addition, we encourage the relaxation of abstract and keyword limitations in journals with strict guidelines, and the inclusion of multilingual abstracts to broaden global accessibility. These recommendations to editors are designed to improve article engagement and facilitate evidence synthesis, thereby aligning scientific publishing with the modern needs of academic research.

Evaluating and Characterizing the Scope of Care for Interventions Labeled as Manual Therapy in Low Back Pain Trials: A Scoping Review

OBJECTIVE

The purpose of this scoping review was to evaluate and characterize the scope of care for low back pain that falls under the specific label of manual therapy.

METHODS

PubMed database, Ovid MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Central Register of Controlled Trials (CENTRAL), and SPORTDiscus were searched from journal inception through May 2022 for randomized controlled trials that investigated the treatment of low back pain using manual therapy. Terminology used to define manual therapy was extracted and categorized by using only the words included in the description of the intervention. An expert consultation phase was undertaken to gather feedback.

RESULTS

One hundred seventy-six trials met final inclusion criteria, and 169 unique terms labeled as manual therapy for the treatment of low back pain were found. The most frequent terms were mobilization (29.0%), manipulation (16.0%), and thrust (6.4%). Eight percent of trials did not define or specify what type of manual therapy was used in the study. After removing duplicates, 169 unique terms emerged within 18 categories.

CONCLUSIONS

Manual therapy intervention labels used in low back pain trials are highly variable. With such variation, the heterogeneity of the intervention in trials is likely large, and the likelihood that different trials are comparing the same interventions is low. Researchers should consider being more judicious with the use of the term manual therapy and provide greater detail in titles, methods, and supplementary appendices in order to improve clarity, clinical applicability, and usefulness of future research.

IMPACT

The ability to interpret and apply findings from manual therapy-related research for low back pain is challenging due to the heterogeneity of interventions under this umbrella term. A clear use of terminology and description of interventions by researchers will allow for improved understanding for the role of manual therapy in managing back pain.

Systematic review search strategies are poorly reported and not reproducible: a cross-sectional metaresearch study

OBJECTIVES

To determine the reproducibility of biomedical systematic review search strategies.

STUDY DESIGN AND SETTING

A cross-sectional reproducibility study was conducted on a random sample of 100 systematic reviews indexed in MEDLINE in November 2021. The primary outcome measure is the percentage of systematic reviews for which all database searches can be reproduced, operationalized as fulfilling six key Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension (PRISMA-S) reporting guideline items and having all database searches reproduced within 10% of the number of original results. Key reporting guideline items included database name, multi-database searching, full search strategies, limits and restrictions, date(s) of searches, and total records.

RESULTS

The 100 systematic review articles contained 453 database searches. Only 22 (4.9%) database searches reported all six PRISMA-S items. Forty-seven (10.4%) database searches could be reproduced within 10% of the number of results from the original search; six searches differed by more than 1,000% between the originally reported number of results and the reproduction. Only one systematic review article provided the necessary search details to be fully reproducible.

CONCLUSION

Systematic review search reporting is poor. To correct this will require a multifaceted response from authors, peer reviewers, journal editors, and database providers.

The Issues with Journal Issues: Let Journals Be Digital Libraries

Science depends on a communication system, and today, that is largely provided by digital technologies such as the internet and web. Despite the fact that digital technologies provide the infrastructure for this communication system, peer-reviewed journals continue to mimic workflows and processes from the print era. This paper focuses on one artifact from the print era, the journal issue, and describes how this artifact has been detrimental to the communication of science, and therefore, to science itself. To replace the journal issue, this paper argues that scholarly publishing and journals could more fully embrace digital technologies by creating digital libraries to present and organize scholarly output.

A suggested data structure for transparent and repeatable reporting of bibliographic searching

Academic searching is integral to research activities: (1) searching to retrieve specific information, (2) to expand our knowledge iteratively, (3) and to collate a representative and unbiased selection of the literature. Rigorous searching methods are vital for reliable, repeatable and unbiased searches needed for these second and third forms of searches (exploratory and systematic searching, respectively) that form a core part of evidence syntheses. Despite the broad awareness of the importance of transparency in reporting search activities in evidence syntheses, the importance of searching has been highlighted only recently and has been the explicit focus of reporting guidance (PRISMA-S). Ensuring bibliographic searches are reported in a way that is transparent enough to allow for full repeatability or evaluation is challenging for a number of reasons. Here, we detail these reasons and provide for the first time a standardised data structure for transparent and comprehensive reporting of search histories. This data structure was produced by a group of international experts in informatics and library sciences. We explain how the data structure was produced and describe its components in detail. We also demonstrate its practical applicability in tools designed to support literature review authors and explain how it can help to improve interoperability across tools used to manage literature reviews. We call on the research community and developers of reference and review management tools to embrace the data structure to facilitate adequate reporting of academic searching in an effort to raise the standard of evidence syntheses globally.

The use of subject headings varied in Embase and MEDLINE: An analysis of indexing across six subject areas

Many bibliographic databases describe the content of a publication using a thesaurus. The vocabularies vary and the extent to which the databases apply them may also differ significantly. The aim of this study is to empirically explore the number of subject headings assigned to publications in two databases over time and to determine if publication characteristics are associated with the number of subject headings. Articles and reviews in MEDLINE and Embase from 1990 to 2019 assigned with one of the subject headings from six subject areas are included in this study. Each of the retrieved publications in Embase is matched with a similar publication in MEDLINE. Furthermore, multivariable linear regressions are used to explore the association of the number of subject headings in MEDLINE and Embase with six prespecified publication characteristics. The average number of assigned subject headings in MEDLINE is stable or even slightly decreasing over time. In Embase, the average number of assigned subject headings was stable until about 2000 where the average number increased dramatically during the next 3 years. Furthermore, linear regressions show that the average number of subject headings in MEDLINE and Embase is higher for publications in English, publications with longer abstract, recent publications and if it belongs to specific subject areas. However, reviews are assigned with more subject headings in Embase and fewer in MEDLINE. The implications of the results are discussed.

A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches

The advent of Question Answering Systems (QASs) has been envisaged as a promising solution and an efficient approach for retrieving significant information over the Internet. A considerable amount of research work has focused on open domain QASs based on deep learning techniques due to the availability of data sources. However, the medical domain receives less attention due to the shortage of medical datasets. Although Electronic Health Records (EHRs) are empowering the field of Medical Question-Answering (MQA) by providing medical information to answer user questions, the gap is still large in the medical domain, especially for textual-based sources. Therefore, in this study, the medical textual question-answering systems based on deep learning approaches were reviewed, and recent architectures of MQA systems were thoroughly explored. Furthermore, an in-depth analysis of deep learning approaches used in different MQA system tasks was provided. Finally, the different critical challenges posed by MQA systems were highlighted, and recommendations to effectively address them in forthcoming MQA systems were given out.