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ObjectivesThe goal of this study is to identify, analyse and classify interventions to improve adherence to reporting guidelines in order to obtain a wide picture of how the problem of enhancing the completeness of reporting of biomedical literature has been tackled so far.DesignScoping review.Search strategyWe searched the MEDLINE, EMBASE and Cochrane Library databases and conducted a grey literature search for (1) studies evaluating interventions to improve adherence to reporting guidelines in health research and (2) other types of references describing interventions that have been performed or suggested but never evaluated. The characteristics and effect of the evaluated interventions were analysed. Moreover, we explored the rationale of the interventions identified and determined the existing gaps in research on the evaluation of interventions to improve adherence to reporting guidelines.Results109 references containing 31 interventions (11 evaluated) were included. These were grouped into five categories: (1) training on the use of reporting guidelines, (2) improving understanding, (3) encouraging adherence, (4) checking adherence and providing feedback, and (5) involvement of experts. Additionally, we identified lack of evaluated interventions (1) on training on the use of reporting guidelines and improving their understanding, (2) at early stages of research and (3) after the final acceptance of the manuscript.ConclusionsThis scoping review identified a wide range of strategies to improve adherence to reporting guidelines that can be taken by different stakeholders. Additional research is needed to assess the effectiveness of many of these interventions.

There is clear guidance on the responsibilities of editors to ensure that the research they publish is of the highest possible quality. Poor reporting is unethical and directly impacts patient care. Reporting guidelines are a relatively recent development to help improve the accuracy, clarity, and transparency of biomedical publications. They have caught on, with hundreds of reporting guidelines now available. Some journals endorse reporting guidelines while a smaller number have used various approaches to implement them. Yet challenges remain – biomedical research is still not optimally reported despite the abundance of reporting guidelines. Electronic algorithms are now being developed to facilitate the choice of correct reporting guideline(s), while other tools are being integrated into journal editorial management processes. Universities need to consider whether it is responsible to advance careers of faculty based on poorly reported research which is of little societal value. If journals embraced auditing of the quality of articles they publish this would give them and their readers essential feedback from which to improve their product.

Background: The quality of reporting of acupuncture interventions is critical to ensure the applicability and reproducibility of acupuncture clinical trials. In the past, different publications have evaluated the quality of reporting of acupuncture interventions for different clinical situations, such as knee osteoarthritis, neurological diseases or cancer. However, this has not been done for acupuncture trials for chronic obstructive pulmonary disease (COPD). Objective: To assess the quality of reporting of acupuncture interventions in trials for COPD. Methods: A total of 11 English and Chinese databases were screened up until May 2019 for randomised or quasi-randomised control trials of acupuncture for COPD. The STRICTA checklist was used to determine the quality of the reporting of acupuncture interventions. Results: A total of 28 trials were included in our review. Out of the 16 STRICTA checklist subitems analysed, only 3 were considered appropriately reported in more than 70% of the trials, while 7 were correctly reported in less than 40%. Conclusion: The adherence to STRICTA guidelines of acupuncture trials for COPD is suboptimal, and future efforts need to be addressed to improve the quality of reporting.

Over 400 reporting guidelines are currently published, but the frequency of their use by authors to accurately and transparently report research remains unclear. This study examined citation counts of reporting guidelines and characteristics contributing to their citation impact. Web of Science database was searched for citation counts of all reporting guidelines with a minimum citation age of 5 years. The total citation impact, mean citation impact and the factors contributing to 2- and 5-year citation rate were established. The search identified 296 articles of reporting guidelines from 1995 to 2013. The mean citations per year was 32.4 (95% confidence interval, 22.3–42.4 citations). The factors associated with 2- and 5-year citation performance of reporting guidelines included the following: open access to the reporting guideline, field of the publishing journal (general vs. specialized medical journal), impact factor of the publishing journal, simultaneous publication in multiple journals, and a male first author. The citation rate across reporting guidelines varied with journal impact factor, open access publication, field of the publishing journal, simultaneous publications, and a male first author. Gaps in citations highlight opportunities to increase visibility and encourage author use of reporting guidelines. •Preferred Reporting Items for Systematic Reviews and Meta-Analyses was most cited (36,407 citations).•Citation impact varied by open access status, journal impact factor, concurrent publications, and a male first author.•Only 1.4% of clinical trials published in major journals cited the CONSORT statement.

Introduction There was limited evidence on the quality of reporting and methodological quality of prediction models using machine learning methods in preterm birth. This systematic review aimed to assess the reporting quality and risk of bias of a machine learning‐based prediction model in preterm birth. Material and methods We conducted a systematic review, searching the PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disk, VIP Database, and WanFang Data from inception to September 27, 2021. Studies that developed (validated) a prediction model using machine learning methods in preterm birth were included. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and Prediction model Risk of Bias Assessment Tool (PROBAST) to evaluate the reporting quality and the risk of bias of included studies, respectively. Findings were summarized using descriptive statistics and visual plots. The protocol was registered in PROSPERO (no. CRD 42022301623). Results Twenty‐nine studies met the inclusion criteria, with 24 development‐only studies and 5 development‐with‐validation studies. Overall, TRIPOD adherence per study ranged from 17% to 79%, with a median adherence of 49%. The reporting of title, , blinding of predictors, sample size justification, explanation of model, and model performance were mostly poor, with TRIPOD adherence ranging from 4% to 17%. For all included studies, 79% had a high overall risk of bias, and 21% had an unclear overall risk of bias. The analysis domain was most commonly rated as high risk of bias in included studies, mainly as a result of small effective sample size, selection of predictors based on univariable analysis, and lack of calibration evaluation. Conclusions Reporting and methodological quality of machine learning‐based prediction models in preterm birth were poor. It is urgent to improve the design, conduct, and reporting of such studies to boost the application of machine learning‐based prediction models in preterm birth in clinical practice. The reporting and methodological quality of machine learning‐based prediction models in preterm birth were poor. It is urgent to improve the design, conduct and reporting of such studies to boost the application of machine learning‐based prediction models in preterm birth in clinical practice.

While previous systematic reviews of trials evaluating conventional antidepressants highlighted inadequacies and inconsistencies in adverse event (AE) reporting, no evaluation is available on esketamine in resistant depression. The objective of this review was to assess quality of reporting AEs in all published clinical trials studying esketamine. It also aimed to compare the proportions of AEs reported in journal articles to those recorded in the ClinicalTrial.gov Registers. Clinical trials evaluating the efficacy and safety of esketamine in depression were searched using Medline and ClinicalTrials.gov. The quality of reporting harms was assessed using a 21-item checklist from the CONSORT Extension of Harms (1 point by item). The total quality score was graded into four categories: high (17–21), moderate (12–16), low (7–11) and very low (0–6). Ten clinical trials were included in the analysis. Nine trials were classified as ‘low quality’ with regard to safety, one trial was classified as ‘moderate quality’. Compared to AEs recorded in ClinicalTrials.gov, we found that 41.5% of serious AEs and 39% of non-serious AEs were not reported in the published articles. Among them, the majority were psychiatric events but also cardiovascular events and 94% concerned patients from esketamine groups. Quality of AEs reporting in published clinical trials of esketamine was poor and harms were reported less frequently in journal publications than in ClinicalTrial.gov Registers. The study suggests that an assessment of the benefits/risks balance of esketamine based on the results reported in trial publications is flawed due to the poor accuracy and completeness of harm data.

Objective To evaluate the usage of a well-known and widely adopted checklist, Checklist for Artificial Intelligence in Medical imaging (CLAIM), for self-reporting through a systematic analysis of its citations. Methods Google Scholar, Web of Science, and Scopus were used to search for citations (date, 29 April 2023). CLAIM’s use for self-reporting with proof (i.e., filled-out checklist) and other potential use cases were systematically assessed in research papers. Eligible papers were evaluated independently by two readers, with the help of automatic annotation. Item-by-item confirmation analysis on papers with checklist proof was subsequently performed. Results A total of 391 unique citations were identified from three databases. Of the 118 papers included in this study, 12 (10%) provided a proof of self-reported CLAIM checklist. More than half (70; 59%) only mentioned some sort of adherence to CLAIM without providing any proof in the form of a checklist. Approximately one-third (36; 31%) cited the CLAIM for reasons unrelated to their reporting or methodological adherence. Overall, the claims on 57 to 93% of the items per publication were confirmed in the item-by-item analysis, with a mean and standard deviation of 81% and 10%, respectively. Conclusion Only a small proportion of the publications used CLAIM as checklist and supplied filled-out documentation; however, the self-reported checklists may contain errors and should be approached cautiously. We hope that this systematic citation analysis would motivate artificial intelligence community about the importance of proper self-reporting, and encourage researchers, journals, editors, and reviewers to take action to ensure the proper usage of checklists. Clinical relevance statement Only a small percentage of the publications used CLAIM for self-reporting with proof (i.e., filled-out checklist). However, the filled-out checklist proofs may contain errors, e.g., false claims of adherence, and should be approached cautiously. These may indicate inappropriate usage of checklists and necessitate further action by authorities. Key Points • Of 118 eligible papers, only 12 (10%) followed the CLAIM checklist for self-reporting with proof (i.e., filled-out checklist). More than half (70; 59%) only mentioned some kind of adherence without providing any proof. • Overall, claims on 57 to 93% of the items were valid in item-by-item confirmation analysis, with a mean and standard deviation of 81% and 10%, respectively. • Even with the checklist proof, the items declared may contain errors and should be approached cautiously.

Background Despite growing recognition of the importance of sex and gender considerations in health research, they are rarely integrated into research design and reporting. We sought to assess the integration of sex, as a biological attribute, and gender, as a socially constructed identity, in published reporting guidelines. Methods We conducted a systematic review of published reporting guidelines listed on the EQUATOR website ( www.equator-nework.org ) from inception until December 2018. We selected all reporting guidelines (original and extensions) listed in the EQUATOR library. We used EndNote Citation Software to build a database of the statements of each guideline identified as a \"full bibliographic reference\" and retrieved the full texts. Reviewers independently extracted the data on use of sex and gender terms from the checklist/abstract/main text of guidelines. Data were analyzed using descriptive statistics and narrative synthesis. Results A total of 407 reporting guidelines were included; they were published between 1995 and 2018. Of the 407 guidelines, 235 (57.7%) mentioned at least one of the sex- and gender-related words. In the checklist of the reporting guidelines ( n  = 363), “sex” and “gender” were mentioned in 50 (13.8%) and 40 (11%), respectively. Only one reporting guideline met our criteria (nonbinary, appropriate categorization, and non-interchangeability) for correct use of sex and gender concepts. Trends in the use of \"sex\" and \"gender\" in the checklists showed that the use of “sex” only started in 2003, while “gender” has been in use since 1996. Conclusions We assessed the integration of sex and gender in reporting guidelines based on the use of sex- and gender-related words. Our findings showed a low use and integration of sex and gender concepts and their incorrect use. Authors of reporting guidelines should reduce this gap for a better use of research knowledge. Trial registration  PROSPERO no. CRD42019136491. Highlights Omission of sex and gender considerations is a recurring deficiency in research design and reporting Integration of sex and gender considerations in health research reporting guidelines is very low Three criteria were used to assess correct use of sex and gender concepts Only one reporting guideline met the three criteria A call to action is made to address these deficiencies

Background Preprint usage is growing rapidly in the life sciences; however, questions remain on the relative quality of preprints when compared to published articles. An objective dimension of quality that is readily measurable is completeness of reporting, as transparency can improve the reader’s ability to independently interpret data and reproduce findings. Methods In this observational study, we initially compared independent samples of articles published in bioRxiv and in PubMed-indexed journals in 2016 using a quality of reporting questionnaire. After that, we performed paired comparisons between preprints from bioRxiv to their own peer-reviewed versions in journals. Results Peer-reviewed articles had, on average, higher quality of reporting than preprints, although the difference was small, with absolute differences of 5.0% [95% CI 1.4, 8.6] and 4.7% [95% CI 2.4, 7.0] of reported items in the independent samples and paired sample comparison, respectively. There were larger differences favoring peer-reviewed articles in subjective ratings of how clearly titles and abstracts presented the main findings and how easy it was to locate relevant reporting information. Changes in reporting from preprints to peer-reviewed versions did not correlate with the impact factor of the publication venue or with the time lag from bioRxiv to journal publication. Conclusions Our results suggest that, on average, publication in a peer-reviewed journal is associated with improvement in quality of reporting. They also show that quality of reporting in preprints in the life sciences is within a similar range as that of peer-reviewed articles, albeit slightly lower on average, supporting the idea that preprints should be considered valid scientific contributions.

Abstract Background In the context of the COVID-19 pandemic, randomized controlled trials (RCTs) are essential to support clinical decision-making. We aimed (1) to assess and compare the reporting characteristics of RCTs between preprints and peer-reviewed publications and (2) to assess whether reporting improves after the peer review process for all preprints subsequently published in peer-reviewed journals. Methods We searched the Cochrane COVID-19 Study Register and L·OVE COVID-19 platform to identify all reports of RCTs assessing pharmacological treatments of COVID-19, up to May 2021. We extracted indicators of transparency (e.g., trial registration, data sharing intentions) and assessed the completeness of reporting (i.e., some important CONSORT items, conflict of interest, ethical approval) using a standardized data extraction form. We also identified paired reports published in preprint and peer-reviewed publications. Results We identified 251 trial reports: 121 (48%) were first published in peer-reviewed journals, and 130 (52%) were first published as preprints. Transparency was poor. About half of trials were prospectively registered ( n = 140, 56%); 38% ( n = 95) made their full protocols available, and 29% ( n = 72) provided access to their statistical analysis plan report. A data sharing statement was reported in 68% ( n = 170) of the reports of which 91% stated their willingness to share. Completeness of reporting was low: only 32% ( n = 81) of trials completely defined the pre-specified primary outcome measures; 57% ( n = 143) reported the process of allocation concealment. Overall, 51% ( n = 127) adequately reported the results for the primary outcomes while only 14% ( n = 36) of trials adequately described harms. Primary outcome(s) reported in trial registries and published reports were inconsistent in 49% ( n = 104) of trials; of them, only 15% ( n = 16) disclosed outcome switching in the report. There were no major differences between preprints and peer-reviewed publications. Of the 130 RCTs published as preprints, 78 were subsequently published in a peer-reviewed journal. There was no major improvement after the journal peer review process for most items. Conclusions Transparency, completeness, and consistency of reporting of COVID-19 clinical trials were insufficient both in preprints and peer-reviewed publications. A comparison of paired reports published in preprint and peer-reviewed publication did not indicate major improvement.