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Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study
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Evaluation of clinical prediction models (part 2): how to undertake an external validation study
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Evaluation of clinical prediction models (part 1): from development to external validation
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Open Science 2.0: Towards a truly collaborative research ecosystem
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OPEN SCIENCE PRACTICES NEED SUBSTANTIAL IMPROVEMENT IN PROGNOSTIC MODEL STUDIES IN ONCOLOGY USING MACHINE LEARNING.
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ACCORD (ACcurate COnsensus Reporting Document): A reporting guideline for consensus methods in biomedicine developed via a modified Delphi
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The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols
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What's fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning: JustEFAB
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Protocol for the development of an artificial intelligence extension to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022
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Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)
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Commentary: Patient Perspectives on Artificial Intelligence; What have We Learned and How Should We Move Forward?
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Machine learning for the prediction of toxicities from head and neck cancer treatment: A systematic review with meta-analysis.
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Systematic review finds "Spin" practices and poor reporting standards in studies on machine learning-based prediction models.
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Machine Learning and Statistics in Clinical Research Articles-Moving Past the False Dichotomy.
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Gynecological cancer prognosis using machine learning techniques: A systematic review of the last three decades (1990-2022)
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Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review
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Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence
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There is no such thing as a validated prediction model
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Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration
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Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist
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Current state and completeness of reporting clinical prediction models using machine learning in systemic lupus erythematosus: A systematic review.
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Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review
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PRISMA AI reporting guidelines for systematic reviews and meta-analyses on AI in healthcare
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Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review
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Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models.
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Tackling bias in AI health datasets through the STANDING Together initiative
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Machine learning in predicting cardiac surgery-associated acute kidney injury: A systemic review and meta-analysis
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Social determinants of health in prognostic machine learning models for orthopaedic outcomes: A systematic review.
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Risk of bias of prognostic models developed using machine learning: a systematic review in oncology
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Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
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Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review
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Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review
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Minimum sample size calculations for external validation of a clinical prediction model with a time‐to‐event outcome
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Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
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Standardized Reporting of Machine Learning Applications in Urology: The STREAM-URO Framework.
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Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review
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Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence
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Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved
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Machine learning in vascular surgery: a systematic review and critical appraisal
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Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
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Minimum sample size for external validation of a clinical prediction model with a binary outcome
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Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal
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Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review
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Equity in essence: a call for operationalising fairness in machine learning for healthcare
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Clinical prediction models: diagnosis versus prognosis.
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Reproducibility in machine learning for health research: Still a ways to go
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Leveraging Open Science to Accelerate Research.
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Machine learning prediction models in orthopedic surgery: A systematic review in transparent reporting
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Health data poverty: an assailable barrier to equitable digital health care.
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Artificial intelligence in dental research: Checklist for Authors, Reviewers, Readers.
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External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb
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Clinician checklist for assessing suitability of machine learning applications in healthcare
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External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature
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Minimum sample size for external validation of a clinical prediction model with a continuous outcome
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Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review
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Recommendations for Reporting Machine Learning Analyses in Clinical Research
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Ethical Machine Learning in Health Care
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Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
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Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
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MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care
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Transparent Reporting of Multivariable Prediction Models in Journal and Conference Abstracts: TRIPOD for Abstracts
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Ethical limitations of algorithmic fairness solutions in health care machine learning.
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Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection
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Presenting machine learning model information to clinical end users with model facts labels
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Calculating the sample size required for developing a clinical prediction model
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Reporting quality of studies using machine learning models for medical diagnosis: a systematic review
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Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers.
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A systematic review of machine learning models for predicting outcomes of stroke with structured data
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Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisal
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Reporting of artificial intelligence prediction models
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A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models.
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PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration
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PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
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Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes
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Minimum sample size for developing a multivariable prediction model: Part I – Continuous outcomes
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Sample size for binary logistic prediction models: Beyond events per variable criteria
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GRIPP2 reporting checklists: tools to improve reporting of patient and public involvement in research
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No rationale for 1 variable per 10 events criterion for binary logistic regression analysis
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Prediction models for cardiovascular disease risk in the general population: systematic review
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Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration
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Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement
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External validation of multivariable prediction models: a systematic review of methodological conduct and reporting
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Reducing waste from incomplete or unusable reports of biomedical research
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Reporting and Methods in Clinical Prediction Research: A Systematic Review
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Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting
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Reporting methods in studies developing prognostic models in cancer: a review
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Guidance for Developers of Health Research Reporting Guidelines
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Predicting Outcome after Traumatic Brain Injury: Development and International Validation of Prognostic Scores Based on Admission Characteristics
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EQUATOR: reporting guidelines for health research
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General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study
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The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women
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Projecting individualized probabilities of developing breast cancer for white females who are being examined annually.
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What is a patient representative?
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Ethics approval This study was conducted with the approval of the East London and City Health Authority Ethic Committee. Provenance and peer review Not commissioned; externally peer reviewed.
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Department of Computer Science, University of Toronto
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, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
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Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA
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Data sharing: Aggregated Delphi survey responses are available on the Open Science Framework
