BMJ Evidence-Based Medicine最新文献

{"title":"Evaluation of reported claims of sex-based differences in treatment effects across meta-analyses: a meta-research study.","authors":"Lum Kastrati, Sara Farina, Angelica Valz Gris, Hamidreza Raeisi-Dehkordi, Erand Llanaj, Hugo G Quezada-Pinedo, Lia Bally, Taulant Muka, John P A Ioannidis","doi":"10.1136/bmjebm-2024-113359","DOIUrl":"10.1136/bmjebm-2024-113359","url":null,"abstract":"<p><strong>Importance: </strong>Differences in treatment effects between men and women may have important implications across diverse interventions and diseases.</p><p><strong>Objectives: </strong>We aimed to evaluate claims of sex-based differences in treatment effects across published meta-analyses.</p><p><strong>Eligibility criteria: </strong>Published meta-analyses of randomised controlled trials (RCTs) that had any mention of sex (male/female) subgroup or related analysis in their abstract INFORMATION SOURCES: PubMed (searched up to 17 January 2024).</p><p><strong>Synthesis: </strong>We determined how many meta-analyses had made claims of sex-based differences in treatment effects. These meta-analyses were examined in depth to determine whether the claims reflected sex-treatment interactions with statistical support or fallacious claims, and we categorised the frequency of different fallacies or genuine interactions. We also investigated how many of the genuine and fallacious claims were considered and discussed in Up-To-Date. Whenever possible, we reanalysed the p value for sex-treatment interaction.</p><p><strong>Main outcomes and measures: </strong>Number of claims with statistical support and fallacious claims; clinical implications of subgroup differences as well as the credibility of subgroup analyses assessed by the Instrument to assess the Credibility of Effect Modification Analyses criteria.</p><p><strong>Results: </strong>216 meta-analysis articles fulfilled the eligibility criteria. Of them, 99 stated in the abstract that there was no sex-based difference, and 20 mentioned a sex-based subgroup analysis but without reporting results in the abstract. The other 97 meta-analyses made 115 claims of sex-based differences. 27 of the 115 positive claims for subgroup differences made across 21 articles had statistical support at p<0.05, of which 4 were mentioned in Up-To-Date, with none leading to different recommendations for men and women. 39 of the 115 positive claims made across 35 articles were fallacious, where the sex-treatment interaction was not statistically significant. The most common form of fallacy (29/115) was made in instances where there was a significant effect in one sex, but not in the other, with no true difference between the two groups. In 7/115 other claims, there were larger effects in one sex, again, with no true difference between the two groups, and 3/115 other claims had various forms of fallacies.Another 44 articles made 49 claims based on potentially fallacious methods (44 based on meta-regression, and 5 provided the results of only one group), but proper data were unavailable to assess statistical significance.</p><p><strong>Conclusions and relevance: </strong>Few meta-analyses of RCTs make claims of sex-based differences in treatment effects, and most of these claims lack formal statistical support. In the present sample, statistically significant and clinically actionable sex-treatment interactions were ra","PeriodicalId":9059,"journal":{"name":"BMJ Evidence-Based Medicine","volume":" ","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Standardised mean difference.","authors":"Daniel Gallardo-Gómez, Kerry Dwan","doi":"10.1136/bmjebm-2024-113579","DOIUrl":"10.1136/bmjebm-2024-113579","url":null,"abstract":"","PeriodicalId":9059,"journal":{"name":"BMJ Evidence-Based Medicine","volume":" ","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Top 15 Choosing Wisely international campaign recommendations to reduce low-value care.","authors":"Wendy Levinson, Karen Born, Juan Victor Ariel Franco, Karin Silvana Kopitowski","doi":"10.1136/bmjebm-2025-113804","DOIUrl":"https://doi.org/10.1136/bmjebm-2025-113804","url":null,"abstract":"","PeriodicalId":9059,"journal":{"name":"BMJ Evidence-Based Medicine","volume":" ","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144224274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evidence categories in systematic assessment of cancer overdiagnosis.","authors":"Anton Barchuk, Niko K Nordlund, Alex L E Halme, Kari A O Tikkinen","doi":"10.1136/bmjebm-2024-113529","DOIUrl":"https://doi.org/10.1136/bmjebm-2024-113529","url":null,"abstract":"<p><p>The phenomenon of cancer overdiagnosis, the diagnosis of a malignant tumour that, without detection, would never lead to adverse health effects, has been reported for several cancer types in different populations. There has been an increase in studies focused on overdiagnosis, creating an opportunity to synthesise evidence on specific cancer types. However, studies that systematically assess evidence across different research domains remain scarce, with most of them relying on data from studies that already mentioned overdiagnosis as a potential concern. In this review, we consider several evidence categories that are used to systematically assess the presence and magnitude of overdiagnosis, including (1) data from cancer surveillance, (2) studies exploring the 'true' prevalence of cancer in the population, (3) studies that explore the use of diagnostics and its effect on incidence and mortality and (4) studies that explore changes and progress in cancer management and its effect on cancer mortality. This article highlights the strengths and weaknesses of different evidence categories, provides examples of studies on different cancer types and discusses how these categories can help synthesise evidence on cancer overdiagnosis.</p>","PeriodicalId":9059,"journal":{"name":"BMJ Evidence-Based Medicine","volume":" ","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Romanticised semiology.","authors":"José Nunes de Alencar, Pedro Henrique Brandão da Silva, Vitor Borin","doi":"10.1136/bmjebm-2025-113664","DOIUrl":"https://doi.org/10.1136/bmjebm-2025-113664","url":null,"abstract":"","PeriodicalId":9059,"journal":{"name":"BMJ Evidence-Based Medicine","volume":" ","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What makes a 'good' decision with artificial intelligence? A grounded theory study in paediatric care.","authors":"Melissa D McCradden, Kelly Thai, Azadeh Assadi, Sana Tonekaboni, Ian Stedman, Shalmali Joshi, Minfan Zhang, Fanny Chevalier, Anna Goldenberg","doi":"10.1136/bmjebm-2024-112919","DOIUrl":"10.1136/bmjebm-2024-112919","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To develop a framework for good clinical decision-making using machine learning (ML) models for interventional, patient-level decisions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Design: &lt;/strong&gt;Grounded theory qualitative interview study.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Setting: &lt;/strong&gt;Primarily single-site at a major urban academic paediatric hospital, with external sampling.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Participants: &lt;/strong&gt;Sixteen participants representing physicians (n=10), nursing (n=3), respiratory therapists (n=2) and an ML specialist (n=1) with experience working in acute care environments were identified through purposive sampling. Individuals were recruited to represent a spectrum of ML knowledge (three expert, four knowledgeable and nine non-expert) and years of experience (median=12.9 years postgraduation). Recruitment proceeded through snowball sampling, with individuals approached to represent a diversity of fields, levels of experience and attitudes towards artificial intelligence (AI)/ML. A member check step and consultation with patients was undertaken to vet the framework, which resulted in some minor revisions to the wording and framing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Interventions: &lt;/strong&gt;A semi-structured virtual interview simulating an intensive care unit handover for a hypothetical patient case using a simulated ML model and seven visualisations using known methods addressing interpretability of models in healthcare. Participants were asked to make an initial care plan for the patient, then were presented with a model prediction followed by the seven visualisations to explore their judgement and potential influence and understanding of the visualisations. Two visualisations contained contradicting information to probe participants' resolution process for the contrasting information. The ethical justifiability and clinical reasoning process were explored.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Main outcome: &lt;/strong&gt;A comprehensive framework was developed that is grounded in established medicolegal and ethical standards and accounts for the incorporation of inference from ML models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We found that for making good decisions, participants reflected across six main categories: evidence, facts and medical knowledge relevant to the patient's condition; how that knowledge may be applied to this particular patient; patient-level, family-specific and local factors; facts about the model, its development and testing; the patient-level knowledge sufficiently represented by the model; the model's incorporation of relevant contextual factors. This judgement was centred on and anchored most heavily on the overall balance of benefits and risks to the patient, framed by the goals of care. We found evidence of automation bias, with many participants assuming that if the model's explanation conflicted with their prior knowledge that their judgement was incorrect; others concluded the exact opposite, drawing from their medical knowledge base to reject the incorrect informati","PeriodicalId":9059,"journal":{"name":"BMJ Evidence-Based Medicine","volume":" ","pages":"183-193"},"PeriodicalIF":9.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12171473/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143405686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing clinical practice guidelines with STAR.","authors":"Nan Yang, Hui Liu, Janne Estill, Yaolong Chen","doi":"10.1136/bmjebm-2024-112893","DOIUrl":"10.1136/bmjebm-2024-112893","url":null,"abstract":"","PeriodicalId":9059,"journal":{"name":"BMJ Evidence-Based Medicine","volume":" ","pages":"212-213"},"PeriodicalIF":9.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142387837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Therapeutic quality of exercise interventions for chronic low back pain: a meta-research study using i-CONTENT tool.","authors":"Ignazio Geraci, Silvia Bargeri, Giacomo Basso, Greta Castellini, Alessandro Chiarotto, Silvia Gianola, Raymond Ostelo, Marco Testa, Tiziano Innocenti","doi":"10.1136/bmjebm-2024-113235","DOIUrl":"10.1136/bmjebm-2024-113235","url":null,"abstract":"<p><strong>Objective: </strong>To assess the therapeutic quality of exercise interventions delivered in chronic low back pain (cLBP) trials using the international Consensus on Therapeutic Exercise aNd Training (i-CONTENT) tool and its inter-rater agreement.</p><p><strong>Methods: </strong>We performed a meta-research study, starting from the trials' arms included in the published Cochrane review (2021) 'Exercise therapy for chronic low back pain'. Two pairs of independent reviewers applied the i-CONTENT tool, a standardised tool designed to ensure the quality of exercise therapy intervention, in a random sample of 100 different exercise arms. We assessed the inter-rater agreement of each category calculating the specific agreement. A percentage of 70% was considered satisfactory.</p><p><strong>Results: </strong>We included 100 arms from 68 randomised controlled trials published between 1991 and 2019. The most assessed exercise types were core strengthening (n=27 arms) and motor control (n=13 arms). Among alternative approaches, yoga (n=11) and Pilates (n=7) were the most representative. Overall, most exercise interventions were rated as having a low risk of ineffectiveness for patient selection (100%), exercise type (92%), outcome type and timing (89%) and qualified supervisor (84%). Conversely, some items showed more uncertainty: the safety of exercise programmes was rated as 'probably low risk' in 58% of cases, exercise dosage in 34% and adherence to exercise in 44%. The items related to exercise dosage (31%) and adherence (29%) had heterogenous judgements, scoring as high risk of ineffectiveness or probably not done. Among all exercise types, Pilates scored best in all domains. A satisfactory specific agreement for 'low risk category' was achieved in all items, except dosage of exercise (60%) and adherence to exercise (54%).</p><p><strong>Conclusion: </strong>Exercises delivered for patients with cLBP generally demonstrate favourable therapeutic quality, although some exercise modalities may present poor therapeutic quality related to dosage and adherence. While the i-CONTENT judgements generally showed satisfactory specific agreement between raters, disagreements arose in evaluating some crucial items.</p>","PeriodicalId":9059,"journal":{"name":"BMJ Evidence-Based Medicine","volume":" ","pages":"194-201"},"PeriodicalIF":9.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Four overlooked errors in ROC analysis: how to prevent and avoid.","authors":"Zhuoqiao He, Qingying Zhang, Manshu Song, Xuerui Tan, Wei Wang","doi":"10.1136/bmjebm-2024-113078","DOIUrl":"10.1136/bmjebm-2024-113078","url":null,"abstract":"","PeriodicalId":9059,"journal":{"name":"BMJ Evidence-Based Medicine","volume":" ","pages":"208-211"},"PeriodicalIF":9.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12171508/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying and counteracting fraudulent responses in online recruitment for health research: a scoping review.","authors":"Josielli Comachio, Adam Poulsen, Adeola Bamgboje-Ayodele, Aidan Tan, Julie Ayre, Rebecca Raeside, Rajshri Roy, Edel O'Hagan","doi":"10.1136/bmjebm-2024-113170","DOIUrl":"10.1136/bmjebm-2024-113170","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to describe how health researchers identify and counteract fraudulent responses when recruiting participants online.</p><p><strong>Design: </strong>Scoping review.</p><p><strong>Eligibility criteria: </strong>Peer-reviewed studies published in English; studies that report on the online recruitment of participants for health research; and studies that specifically describe methodologies or strategies to detect and address fraudulent responses during the online recruitment of research participants.</p><p><strong>Sources of evidence: </strong>Nine databases, including Medline, Informit, AMED, CINAHL, Embase, Cochrane CENTRAL, IEEE Xplore, Scopus and Web of Science, were searched from inception to April 2024.</p><p><strong>Charting methods: </strong>Two authors independently screened and selected each study and performed data extraction, following the Joanna Briggs Institute's methodological guidance for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. A predefined framework guided the evaluation of fraud identification and mitigation strategies within the studies included. This framework, adapted from a participatory mapping study that identified indicators of fraudulent survey responses, allowed for systematic assessment and comparison of the effectiveness of various antifraud strategies across studies.</p><p><strong>Results: </strong>23 studies were included. 18 studies (78%) reported encountering fraudulent responses. Among the studies reviewed, the proportion of participants excluded for fraudulent or suspicious responses ranged from as low as 3% to as high as 94%. Survey completion time was used in six studies (26%) to identify fraud, with completion times under 5 min flagged as suspicious. 12 studies (52%) focused on non-confirming responses, identifying implausible text patterns through specific questions, consistency checks and open-ended questions. Four studies examined temporal events, such as unusual survey completion times. Seven studies (30%) reported on geographical incongruity, using IP address verification and location screening. Incentives were reported in 17 studies (73%), with higher incentives often increasing fraudulent responses. Mitigation strategies included using in-built survey features like Completely Automated Public Turing test to tell Computers and Humans Apart (34%), manual verification (21%) and video checks (8%). Most studies recommended multiple detection methods to maintain data integrity.</p><p><strong>Conclusion: </strong>There is insufficient evaluation of strategies to mitigate fraud in online health research, which hinders the ability to offer evidence-based guidance to researchers on their effectiveness. Researchers should employ a combination of strategies to counteract fraudulent responses when recruiting online to optimise data integrity.</p>","PeriodicalId":9059,"journal":{"name":"BMJ Evidence-Based Medicine","volume":" ","pages":"173-182"},"PeriodicalIF":9.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12171401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142881266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}