BMJ Evidence-Based Medicine最新文献

{"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":"Standardised mean difference.","authors":"Daniel Gallardo-Gómez, Kerry Dwan","doi":"10.1136/bmjebm-2024-113579","DOIUrl":"https://doi.org/10.1136/bmjebm-2024-113579","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":"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":"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":"","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":"","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":"","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":"","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":"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":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Informatics hygiene to support reuse of routinely collected health care data for evidence-based practice.","authors":"Elmer V Bernstam, Alejandro Araya, Matthew Decaro, Todd R Johnson","doi":"10.1136/bmjebm-2024-112948","DOIUrl":"10.1136/bmjebm-2024-112948","url":null,"abstract":"","PeriodicalId":9059,"journal":{"name":"BMJ Evidence-Based Medicine","volume":" ","pages":"145-148"},"PeriodicalIF":9.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698945/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141533562","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":"Perspectives of clinicians and screening candidates on shared decision-making in prostate cancer screening with the prostate-specific antigen (PSA) test: a qualitative study (PROSHADE study).","authors":"María José Sanchis, Mercedes Guilabert, Lucy A Parker, Juan Pablo Caballero-Romeu, Elisa Chilet-Rosell, Luis Gómez-Pérez, Pablo Alonso-Coello, Ana Cebrián, Maite López-Garrigós, Irene Moral, Elena Ronda-Pérez, Carlos Canelo-Aybar, Ildefonso Hernández-Aguado, Ignacio Párraga, María Del Campo-Giménez, Blanca Lumbreras","doi":"10.1136/bmjebm-2024-113113","DOIUrl":"10.1136/bmjebm-2024-113113","url":null,"abstract":"<p><strong>Objective: </strong>The objective of this study is to analyse the perspectives of screening candidates and healthcare professionals on shared decision-making (SDM) in prostate cancer (PCa) screening using the prostate-specific antigen (PSA) test.</p><p><strong>Design: </strong>Descriptive qualitative study (May-December 2022): six face-to-face focus groups and four semistructured interviews were conducted, transcribed verbatim and thematically analysed using ATLAS.ti software.</p><p><strong>Setting: </strong>Data were obtained as part of the project PROSHADE (Decision Aid for Promoting Shared Decision Making in Opportunistic Screening for Prostate Cancer) to develop a tool for SDM in PCa screening with PSA testing in Spain.</p><p><strong>Participants: </strong>A total of 27 screening candidates (three groups of men: 40-50 years old; 51-60 years old and 61-80 years old), 25 primary care professionals (one group of eight nurses and two groups of physicians: one with more and one with less than 10 years of experience), and four urologists. Focus groups for patients and healthcare professionals were conducted separately.</p><p><strong>Main outcome measures: </strong>Participants' perceptions of shared decision-making related to PSA opportunistic screening, including their understanding, preferences, and attitudes.</p><p><strong>Results: </strong>Three themes were generated: (1) perceptions of SDM, (2) perceptions of PSA testing and (3) perceptions of SDM regarding PCa screening. Theme 1: screening candidates valued SDM when it included clear information and empowered them. There was consensus with primary care health professionals on this point, although their knowledge and implementation of SDM varied. Theme 2: candidates were divided on PSA testing; some trusted it for early detection, while others expressed scepticism due to concerns about false positives and invasive procedures, reflecting gaps in accessible information. Theme 3: professionals across primary and specialised care stressed the need for standardised SDM protocols. Primary care physicians were particularly concerned that PSA decisions align with scientific evidence and urologists recognised SDM as valuable in PSA testing only if it was adequately explained to each patient. Barriers to implementing SDM included insufficient coordination across care levels, lack of consensus-driven protocols and limited clinical time.</p><p><strong>Conclusions: </strong>While patients expect comprehensive information, primarily based on practice to achieve empowerment, healthcare professionals face obstacles such as limited time and insufficient coordination between primary care and urology. All stakeholders agree on the importance of evidence-based tools to reinforce effective SDM and enhance collaboration across urologists and primary care in the context of PSA testing.</p>","PeriodicalId":9059,"journal":{"name":"BMJ Evidence-Based Medicine","volume":" ","pages":"163-172"},"PeriodicalIF":9.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142963750","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}