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ObjectivePeer reviewers of biomedical journals are expected to perform a large number of roles and tasks, some of which are seemingly contradictory or demonstrate incongruities between the respective positions of peer reviewers and journal editors. Our aim was to explore the perspectives, expectations and understanding of the roles and tasks of peer reviewers of journal editors from general and specialty biomedical journals.DesignQualitative study.SettingWorldwide.Participants56 journal editors from biomedical journals, most of whom were editors-in-chief (n=39), male (n=40) and worked part-time (n=50) at journals from 22 different publishers.MethodsSemistructured interviews with journal editors were conducted. Recruitment was based on purposive maximum variation sampling. Data were analysed thematically following the methodology by Braun and Clarke.ResultsJournal editors’ understanding of the roles and partly of tasks of peer reviewers are profoundly shaped by each journal’s unique context and characteristics, including financial and human resources and journal reputation or prestige. There was a broad agreement among journal editors on expected technical tasks of peer reviewers related to scientific aspects, but there were different expectations in the level of depth. We also found that most journal editors support the perspective that authorship experience is key to high-quality reviews, while formal training in peer reviewing is not.ConclusionThese journal editors’ accounts reveal issues of a social nature within the peer-review process related to missed opportunities for journal editors to engage with peer reviewers to clarify the expected roles and tasks.Further research is needed on actual performance of peer reviewers looking into the content of peer-reviewer reports to inform meaningful training interventions, journal policies and guidelines.

ObjectivesWe sought to evaluate the characteristics and publication fate of improperly registered clinical trials submitted to a medical journal (The BMJ) over a 4-year period to identify common types of registration issues and their relation to publication outcomes.DesignResearch articles submitted to The BMJ and identified as unregistered or retrospectively registered by editors were included if they reported outcomes of a clinical trial. Relevant data regarding the trials were then extracted from each paper. Trials were categorised as prospectively registered, registered in an unapproved registry, unregistered or other, and explanations for registration deficiencies were grouped into six categories. We searched PubMed and Google to determine whether, where and when improperly registered studies were subsequently published and whether registration issues were disclosed.Results123 research papers reporting apparently unregistered or retrospectively registered clinical trials were identified. 110 studies (89.4%) were retrospectively registered, nine (7.3%) were unregistered, three (2.4%) had been registered in an unapproved registry and one study originally lacking registration details was later discovered to have been prospectively registered. 82 studies (66.6%) were funded entirely or in part by government sources, and only seven studies (5.7%) received funding from industry. Of those papers submitted to The BMJ through the end of 2015, 67 of the 70 papers rejected for registration problems (95.7%) were subsequently published in another journal. The registration problem was disclosed in only 2 (2.9%).ConclusionsImproper registration remains a problem, particularly for clinical trials that are government or foundation-funded. Nonetheless, improperly registered trials are almost always published, suggesting that medical journal editors may not actively enforce registration requirements.

IntroductionThe primary functions of peer reviewers are poorly defined. Thus far no body of literature has systematically identified the roles and tasks of peer reviewers of biomedical journals. A clear establishment of these can lead to improvements in the peer review process. The purpose of this scoping review is to determine what is known on the roles and tasks of peer reviewers.MethodsWe will use the methodological framework first proposed by Arksey and O’Malley and subsequently adapted by Levac et al and the Joanna Briggs Institute. The scoping review will include all study designs, as well as editorials, commentaries and grey literature. The following eight electronic databases will be searched (from inception to May 2017): Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, Educational Resources Information Center, EMBASE, MEDLINE, PsycINFO, Scopus and Web of Science. Two reviewers will use inclusion and exclusion criteria based on the ‘Population–Concept–Context’ framework to independently screen titles and abstracts of articles considered for inclusion. Full-text screening of relevant eligible articles will also be carried out by two reviewers. The search strategy for grey literature will include searching in websites of existing networks, biomedical journal publishers and organisations that offer resources for peer reviewers. In addition we will review journal guidelines to peer reviewers on how to perform the manuscript review. Journals will be selected using the 2016 journal impact factor. We will identify and assess the top five, middle five and lowest-ranking five journals across all medical specialties.Ethics and disseminationThis scoping review will undertake a secondary analysis of data already collected and does not require ethical approval. The results will be disseminated through journals and conferences targeting stakeholders involved in peer review in biomedical research.

Background and objective: There is persistent men-dominated gender disparity in medical academia. Predominance of men in the editorial makeup of medical journals might contribute to this inequity. This retrospective study (2014-2019) sought to evaluate gender representation in reviewers, editors, and members of the editorial boards in 15 leading medical journals from the United States, Canada, and the United Kingdom. Methods: We surveyed lists of reviewers, editors, and editorial board members from seven journals of internal medicine, a specialty dominated by men; three journals of obstetrics and gynaecology and two of paediatrics, specialties dominated by women; and three journals of psychiatry, a gender-balanced specialty. Information from publicly available resources was used to infer gender, and the percentages of women were calculated. Trends over time were characterized by changes in these percentages from year to year through the linear regression line fitted to the data for each journal. Results: Journals of women-dominated specialties had significantly higher proportions of women reviewers than those of men-dominated or gender-balanced specialties, with mean percentages (95% confidence interval) of 45.8% (40.5%-51.1%), 28.0% (22.3%-33.7%), and 33.8% (27.6%-40.1%), respectively (p <0.001). The proportion of women editors and editorial board members showed no statistically significant differences across the three specialties, and the percentage of women reviewers, editors, and editorial board members increased only slightly over time. Conclusion: These results suggest that the fifteen journals are yet to achieve gender parity in their reviewers, editors, and editorial board members, and continued efforts are needed to achieve gender balance in those three groups of medical academia.

IntroductionThe Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques.Methods and analysisTRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation.Ethics and disseminationEthical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications.PROSPERO registration numberCRD42019140361 and CRD42019161764.

Open access publication rates have been steadily increasing over time. In spite of this growth, academics in low income settings struggle to gain access to the full canon of research literature. While the vast majority of open access repositories and funding organizations with open access policies are based in high income countries, the geographic patterns of open access publication itself are not well characterized. In this study, we developed a computational approach to better understand the topical and geographical landscape of open access publications in the biomedical research literature. Surprisingly, we found a strong negative correlation between country per capita income and the percentage of open access publication. Open access publication rates were particularly high in sub-Saharan Africa, but vastly lower in the Middle East and North Africa, South Asia, and East Asia and the Pacific. These effects persisted when considering papers only bearing authors from within each region and income group. However, papers resulting from international collaborations did have a higher percentage of OA than single-country papers, and inter-regional collaboration increased OA publication for all world regions. There was no clear relationship between the number of open access policies in a region and the percentage of open access publications in that region. To understand the distribution of open access across topics of biomedical research, we examined keywords that were most enriched and depleted in open access papers. Keywords related to genomics, computational biology, animal models, and infectious disease were enriched in open access publications, while keywords related to the environment, nursing, and surgery were depleted in open access publications. This work identifies geographic regions and fields of research that could be priority areas for open access advocacy. The finding that open access publication rates are highest in sub-Saharan Africa and low income countries suggests that factors other than open access policy strongly influence authors' decisions to make their work openly accessible. The high proportion of OA resulting from international collaborations indicates yet another benefit of collaborative research. Certain applied fields of medical research, notably nursing, surgery, and environmental fields, appear to have a greater proportion of fee-for-access publications, which presumably creates barriers that prevent researchers and practitioners in low income settings from accessing the literature in those fields.

Diagnostic accuracy studies are, like other clinical studies, at risk of bias due to shortcomings in design and conduct, and the results of a diagnostic accuracy study may not apply to other patient groups and settings. Readers of study reports need to be informed about study design and conduct, in sufficient detail to judge the trustworthiness and applicability of the study findings. The STARD statement (Standards for Reporting of Diagnostic Accuracy Studies) was developed to improve the completeness and transparency of reports of diagnostic accuracy studies. STARD contains a list of essential items that can be used as a checklist, by authors, reviewers and other readers, to ensure that a report of a diagnostic accuracy study contains the necessary information. STARD was recently updated. All updated STARD materials, including the checklist, are available at http://www.equator-network.org/reporting-guidelines/stard. Here, we present the STARD 2015 explanation and elaboration document. Through commented examples of appropriate reporting, we clarify the rationale for each of the 30 items on the STARD 2015 checklist, and describe what is expected from authors in developing sufficiently informative study reports.

Background Narrative reviews are the commonest type of articles in the medical literature. However, unlike systematic reviews and randomized controlled trials (RCT) articles, for which formal instruments exist to evaluate quality, there is currently no instrument available to assess the quality of narrative reviews. In response to this gap, we developed SANRA, the Scale for the Assessment of Narrative Review Articles. Methods A team of three experienced journal editors modified or deleted items in an earlier SANRA version based on face validity, item-total correlations, and reliability scores from previous tests. We deleted an item which addressed a manuscript’s writing and accessibility due to poor inter-rater reliability. The six items which form the revised scale are rated from 0 (low standard) to 2 (high standard) and cover the following topics: explanation of (1) the importance and (2) the aims of the review, (3) literature search and (4) referencing and presentation of (5) evidence level and (6) relevant endpoint data. For all items, we developed anchor definitions and examples to guide users in filling out the form. The revised scale was tested by the same editors (blinded to each other’s ratings) in a group of 30 consecutive non-systematic review manuscripts submitted to a general medical journal. Results Raters confirmed that completing the scale is feasible in everyday editorial work. The mean sum score across all 30 manuscripts was 6.0 out of 12 possible points (SD 2.6, range 1–12). Corrected item-total correlations ranged from 0.33 (item 3) to 0.58 (item 6), and Cronbach’s alpha was 0.68 (internal consistency). The intra-class correlation coefficient (average measure) was 0.77 [95% CI 0.57, 0.88] (inter-rater reliability). Raters often disagreed on items 1 and 4. Conclusions SANRA’s feasibility, inter-rater reliability, homogeneity of items, and internal consistency are sufficient for a scale of six items. Further field testing, particularly of validity, is desirable. We recommend rater training based on the “explanations and instructions” document provided with SANRA. In editorial decision-making, SANRA may complement journal-specific evaluation of manuscripts—pertaining to, e.g., audience, originality or difficulty—and may contribute to improving the standard of non-systematic reviews.

Many indices have been developed to assess the impact of scientific publications by investigators, disciplines, and institutions. The h-index has emerged as a leading tool in the assessment of the productivity of authors. Differences in publication and citation opportunity among specialties create inappropriate conclusions when the h-index is used to compare authors across different disciplines. An alternative, the Radicchi index, [h.sub.f], has been proposed to assess the impact of publications across disciplines. We curated a database of all articles published from 2002 to 2015 from the 3 highest impact factor medical journals: New England Journal of Medicine (NEJM), the Lancet, and the Journal of American Medical Association (JAMA). With this database, articles were categorized into medical subspecialties. We calculated the respective h-index and Radicchi index of each specialty. We found that the Radicchi index eliminated variability associated with publication and citation opportunity between different specialties when compared to the h-index. The Radicchi index is a useful measure of scientific impact and productivity that advances the h-index by allowing interspecialty comparisons. There remains a need to define a researcher's specialty designation especially if he/she conducts multidisciplinary research.