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of Oxford",[130,131,111],"{\"ror\":\"052gg0110\",\"openalex\":\"I40120149\",\"wikidata\":\"Q34433\"}","2026-05-14T00:29:51.401144253","2026-06-01T04:11:03.906541976","companyType","UNIVERSITY","foundedDate","headquarters","country","GB","employeeCount","fundingStage","totalFunding","valuation","website","stockSymbol","uscc","crunchbaseUrl","Oxford University","Prifysgol Rhydychen","company_I2801809904","Nuffield Orthopaedic Centre","{\"openalex\":\"I2801809904\"}","2026-05-14T01:57:26.360713063","2026-05-23T08:23:11.289065454","COMPANY","company_I4210147381","Oxford University Hospitals NHS Trust","{\"openalex\":\"I4210147381\"}","2026-05-14T03:12:30.248514595","2026-05-23T09:57:29.502575879","company_I4210146357","Oxford BioMedica (United Kingdom)",[144],"{\"ror\":\"03dp0vf82\",\"openalex\":\"I4210146357\"}","2026-05-14T03:22:38.384106761","2026-06-01T07:32:18.346075156","Oxford, United Kingdom","http://www.oxfordbiomedica.co.uk/","company_I153267046","University of Kaiserslautern",[160,161,152],"{\"ror\":\"04zrf7b53\",\"openalex\":\"I153267046\",\"wikidata\":\"https://www.wikidata.org/wiki/Q678965\"}","2026-05-14T01:09:45.243823253","2026-06-01T05:24:39.808246341","Kaiserslautern, Germany","DE","http://www.uni-kl.de/no_cache/en/home/","Kaiserslautern University of Technology","Technische Universität Kaiserslautern","company_I1327095140","Office for National Statistics",[163,171],"{\"openalex\":\"I1327095140\"}","2026-05-14T07:40:55.292889872","2026-05-19T05:04:05.371973503","GOVERNMENT","Newport, United Kingdom","http://www.ons.gov.uk/ons/index.html","Swyddfa Ystadegau Gwladol","company_I2802742124","John Radcliffe 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learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness","{\"openalex\":\"W3011403448\",\"doi\":\"10.1136/bmj.l6927\"}","2026-05-16T10:16:52.452000724","2026-05-19T05:04:04.431502482","doi","10.1136/bmj.l6927","arxivId","openalexId","W3011403448","abstractText","Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit.","publishedDate","2020-03-20","publishedYear",2020,"venue","BMJ",465,"authors","keywords",[211,212,213],"pdfUrl","topVenue","Artificial Intelligence in Healthcare and Education","Explainable Artificial Intelligence (XAI)","Machine Learning in Healthcare","paper_W4280572991","Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","{\"openalex\":\"W4280572991\",\"doi\":\"10.1038/s41591-022-01772-9\"}","2026-05-16T09:36:04.230222986","2026-05-19T04:47:21.187509829","10.1038/s41591-022-01772-9","W4280572991","2022-05-01",2022,"Nature Medicine",468,[211,212,213],"https://pure.amsterdamumc.nl/ws/files/139852420/Reporting-guideline-for-the-early-stage-clinical-evaluation-of-decision-support-systems-driven-by-artificial-intelligenc.pdf","paper_W4390674087","Evaluation of clinical prediction models (part 1): from development to external validation","{\"openalex\":\"W4390674087\",\"doi\":\"10.1136/bmj-2023-074819\"}","2026-05-16T06:51:04.228452371","2026-05-19T03:03:47.190436495","10.1136/bmj-2023-074819","W4390674087","Evaluating the performance of a clinical prediction model is crucial to establish its predictive accuracy in the populations and settings intended for use. In this article, the first in a three part series, Collins and colleagues describe the importance of a meaningful evaluation using internal, internal-external, and external validation, as well as exploring heterogeneity, fairness, and generalisability in model performance.","2024-01-08",2024,484,[213,240,241],"https://pure-oai.bham.ac.uk/ws/files/223789914/bmj-2023-074819.full.pdf","Sepsis Diagnosis and Treatment","Meta-analysis and systematic reviews","paper_W2296761362","Adequate sample size for developing prediction models is not simply related to events per variable","{\"openalex\":\"W2296761362\",\"doi\":\"10.1016/j.jclinepi.2016.02.031\"}","2026-05-16T02:23:53.219477796","2026-05-21T19:23:36.348312962","10.1016/j.jclinepi.2016.02.031","W2296761362","OBJECTIVES: The choice of an adequate sample size for a Cox regression analysis is generally based on the rule of thumb derived from simulation studies of a minimum of 10 events per variable (EPV). One simulation study suggested scenarios in which the 10 EPV rule can be relaxed. The effect of a range of binary predictors with varying prevalence, reflecting clinical practice, has not yet been fully investigated. STUDY DESIGN AND SETTING: We conducted an extended resampling study using a large general-practice data set, comprising over 2 million anonymized patient records, to examine the EPV requirements for prediction models with low-prevalence binary predictors developed using Cox regression. The performance of the models was then evaluated using an independent external validation data set. We investigated both fully specified models and models derived using variable selection. RESULTS: Our results indicated that an EPV rule of thumb should be data driven and that EPV ≥ 20 generally eliminates bias in regression coefficients when many low-prevalence predictors are included in a Cox model. CONCLUSION: Higher EPV is needed when low-prevalence predictors are present in a model to eliminate bias in regression coefficients and improve predictive accuracy.","2016-03-08",2016,"Journal of Clinical Epidemiology",514,[240,213,256],"http://nrl.northumbria.ac.uk/id/eprint/34648/1/Adequate%20sample%20size.pdf","Advanced Causal Inference Techniques","paper_W2474667434","External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges","{\"openalex\":\"W2474667434\",\"doi\":\"10.1136/bmj.i3140\"}","2026-05-15T19:34:33.413090539","2026-05-21T14:22:34.188040834","10.1136/bmj.i3140","W2474667434","Access to big datasets from e-health records and individual participant data (IPD) meta-analysis is signalling a new advent of external validation studies for clinical prediction models. In this article, the authors illustrate novel opportunities for external validation in big, combined datasets, while drawing attention to methodological challenges and reporting issues.","2016-06-22",550,[213,241,269],"https://www.bmj.com/content/bmj/353/bmj.i3140.full.pdf","Blood Pressure and Hypertension Studies","paper_W2809949684","Sample size for binary logistic prediction models: Beyond events per variable criteria","{\"openalex\":\"W2809949684\",\"doi\":\"10.1177/0962280218784726\"}","2026-05-15T03:02:18.473622364","2026-05-21T01:53:49.186455958","10.1177/0962280218784726","W2809949684","Binary logistic regression is one of the most frequently applied statistical approaches for developing clinical prediction models. Developers of such models often rely on an Events Per Variable criterion (EPV), notably EPV ≥10, to determine the minimal sample size required and the maximum number of candidate predictors that can be examined. We present an extensive simulation study in which we studied the influence of EPV, events fraction, number of candidate predictors, the correlations and distributions of candidate predictor variables, area under the ROC curve, and predictor effects on out-of-sample predictive performance of prediction models. The out-of-sample performance (calibration, discrimination and probability prediction error) of developed prediction models was studied before and after regression shrinkage and variable selection. The results indicate that EPV does not have a strong relation with metrics of predictive performance, and is not an appropriate criterion for (binary) prediction model development studies. We show that out-of-sample predictive performance can better be approximated by considering the number of predictors, the total sample size and the events fraction. We propose that the development of new sample size criteria for prediction models should be based on these three parameters, and provide suggestions for improving sample size determination.","2018-07-03",2018,"Statistical Methods in Medical Research",734,[284,213,285],"https://dspace.library.uu.nl/handle/1874/387109","Statistical Methods in Epidemiology","Reliability and Agreement in Measurement","paper_W3217399827","Quantifier elimination for real closed fields by cylindrical algebraic decomposition","{\"openalex\":\"W3217399827\"}","2026-05-14T17:41:08.318299062","2026-05-21T22:26:46.183846001","W3217399827","1975-01-01",1975,"Medical Entomology and Zoology",902,[297],"Polynomial and algebraic computation","paper_W3013681994","Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies","{\"openalex\":\"W3013681994\",\"doi\":\"10.1136/bmj.m689\"}","2026-05-14T13:23:21.205661572","2026-05-21T17:28:27.185832985","10.1136/bmj.m689","W3013681994","OBJECTIVE: To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. DESIGN: Systematic review. DATA SOURCES: Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax. REVIEW METHODS: Adherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies. RESULTS: Only 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal (<50% adherence for 12 of 29 TRIPOD items). 61 of 81 studies stated in their abstract that performance of artificial intelligence was at least comparable to (or better than) that of clinicians. Only 31 of 81 studies (38%) stated that further prospective studies or trials were required. CONCLUSIONS: Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions. STUDY REGISTRATION: PROSPERO CRD42019123605.","2020-03-25",1028,[211,310,311],"https://www.bmj.com/content/bmj/368/bmj.m689.full.pdf","COVID-19 diagnosis using AI","AI in cancer detection","paper_W2913997948","A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models","{\"openalex\":\"W2913997948\",\"doi\":\"10.1016/j.jclinepi.2019.02.004\"}","2026-05-14T04:55:59.181544154","2026-05-21T07:14:44.184368731","10.1016/j.jclinepi.2019.02.004","W2913997948","2019-02-11",2019,1886,[211,213,212],"paper_W4233026002","Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","{\"openalex\":\"W4233026002\",\"doi\":\"10.1136/bmj.g7594\"}","2026-05-14T01:57:26.303765706","2026-05-21T03:40:43.182366895","10.1136/bmj.g7594","W4233026002","Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web based survey and revised during a three day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). To encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site (www.annals.org) and will be also published in BJOG, British Journal of Cancer, British Journal of Surgery, BMC Medicine, The BMJ, Circulation, Diabetic Medicine, European Journal of Clinical Investigation, European Urology, and Journal of Clinical Epidemiology. The authors jointly hold the copyright of this article. 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Gary S. Collins 人物

Gary S. Collins · 相关文章