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Search results (20)

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Peer review of prediction model studies in oncology needs improvement: A systematic review of open peer review reports from BMC journals.

Ma J. et al, (2025), J Clin Epidemiol, 188

Peer review reports of randomized controlled trials in oncology can be short and superficial.

Logullo P. et al, (2025), J Clin Epidemiol, 185

Development of a multicentre cohort study to understand the role of MRI and ultrasound in the diagnosis of acute haematogenous bone and joint infection in children (the PIC Bone study) : a study protocol.

Nogaro M-C. et al, (2025), Bone Jt Open, 6, 677 - 684

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

Ma J. et al, (2023), J Clin Epidemiol, 161, 140 - 151

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

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, 158, 99 - 110

Multivariable prediction models for atrial fibrillation after cardiac surgery: a systematic review protocol.

Fields KG. et al, (2023), BMJ Open, 13

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

Andaur Navarro CL. et al, (2023), J Clin Epidemiol, 154, 8 - 22

Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review.

Andaur Navarro CL. et al, (2022), BMC Med Res Methodol, 22

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review.

Andaur Navarro CL. et al, (2021), BMJ, 375

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.

Collins GS. et al, (2021), BMJ Open, 11

Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review

Andaur Navarro CL. et al, (2021)

There are no shortcuts in the development and validation of a COVID-19 prediction model.

Collins GS. et al, (2021), Transbound Emerg Dis, 68, 210 - 211

Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques.

Andaur Navarro CL. et al, (2020), BMJ Open, 10

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