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Publications

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.

Collins GS. et al, (2024), Bmj, 385

The rate of nonunion in the MRI-detected occult scaphoid fracture

Dean BJF. et al, (2024), The bone & joint journal, 106-B, 387 - 393

Prediction model protocols indicate better adherence to recommended guidelines for study conduct and reporting.

Dhiman P. et al, (2024), J clin epidemiol

ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies

Barreñada L. et al, (2024), Bmj medicine, 3, e000817 - e000817

Evaluation of clinical prediction models (part 2): how to undertake an external validation study.

Riley RD. et al, (2024), Bmj, 384

Clinical prediction models and the multiverse of madness.

Riley RD. et al, (2023), Bmc med, 21

Harnessing the potential of data-driven strategies to optimise transfusion practice.

Evans HG. et al, (2023), Br j haematol

Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed.

Levis B. et al, (2023), J clin epidemiol, 165

Open science practices need substantial improvement in prognostic model studies in oncology using machine learning.

Collins GS. et al, (2023), J clin epidemiol, 165

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

Dhiman P. et al, (2023), Nature machine intelligence, 5, 816 - 817

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review.

Dhiman P. et al, (2023), Bmc med res methodol, 23

Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review.

Ma J. et al, (2023), J clin epidemiol

The ADNEX risk prediction model for ovarian cancer diagnosis: A systematic review and meta-analysis of external validation studies

BARREÑADA L. et al, (2023)

Systematic review highlights high risk of bias of clinical prediction models for blood transfusion in patients undergoing elective surgery.

Dhiman P. et al, (2023), J clin epidemiol, 159, 10 - 30

Systematic review finds "Spin" practices and poor reporting standards in studies on machine learning-based prediction models.

Andaur Navarro CL. et al, (2023), J clin epidemiol

Reporting guidelines used varying methodology to develop recommendations.

Schlussel MM. et al, (2023), J clin epidemiol, 159, 246 - 256

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review

Dhiman P. et al, (2023), Journal of clinical epidemiology

Artificial intelligence in lung cancer diagnostic imaging: a review of the reporting and conduct of research published 2018-2019.

Logullo P. et al, (2023), Bjr open, 5

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models.

Andaur Navarro CL. et al, (2022), J clin epidemiol

Risk of bias of prognostic models developed using machine learning: a systematic review in oncology.

Dhiman P. et al, (2022), Diagn progn res, 6

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