The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols

Letter Multidisciplinary Sciences

University vice-chancellor responds

Ben Van Calster

NATURE (2023)

Article Medicine, General & Internal

Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)

Kym I. E. Snell et al.

Summary: Most clinical fields have numerous studies developing or validating prediction models for diagnosis or prognosis. Systematic reviews and meta-analyses are necessary to evaluate and summarize the evidence on the predictive performance of existing models. This article introduces a new reporting guideline to ensure complete, transparent, and accurate reporting of such reviews.

BMJ-BRITISH MEDICAL JOURNAL (2023)

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Letter Biochemistry & Molecular Biology

Tackling bias in AI health datasets through the STANDING Together initiative

Shaswath Ganapathi et al.

NATURE MEDICINE (2022)

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Article Health Care Sciences & Services

Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved

Paula Dhiman et al.

Summary: The study evaluated the completeness of reporting of prognostic prediction models in oncology developed using machine learning methods, revealing that the reporting quality was generally poor, especially in development studies, indicating an urgent need for improvement.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2021)

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Article Medicine, General & Internal

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

Gary S. Collins et al.

Summary: This paper outlines the development process of TRIPOD-AI and PROBAST-AI, including systematic reviews, Delphi process, virtual consensus meetings, and tool development, in five stages. The aim is to provide reporting guidelines and a standardized tool for the critical appraisal of machine learning based prediction model studies.

BMJ OPEN (2021)

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Review Medical Informatics