Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA) by Snell, KIE, Levis, B, Damen, JAA, Dhiman, P, Debray, TPA, Hooft, L, Reitsma, JB, Moons, KGM, Collins, GS, Riley, RD

Systematic review finds "spin" practices and poor reporting standards in studies on machine learning-based prediction models by Andaur Navarro, CL, Damen, JAA, Takada, T, Nijman, SWJ, Dhiman, P, Ma, J, Collins, GS, Bajpai, R, Riley, RD, Moons, KGM, Hooft, L

Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review by Andaur Navarro, CL, Damen, JAA, Takada, T, Nijman, SWJ, Dhiman, P, Ma, J, Collins, GS, Bajpai, R, Riley, RD, Moons, KGM, Hooft, L

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review by Andaur Navarro, CL, Damen, JAA, Takada, T, Nijman, SWJ, Dhiman, P, Ma, J, Collins, GS, Bajpai, R, Riley, RD, Moons, KGM, Hooft, L

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models by Andaur Navarro, CL, Damen, JAA, van Smeden, M, Takada, T, Nijman, SWJ, Dhiman, P, Ma, J, Collins, GS, Bajpai, R, Riley, RD, Moons, KGM, Hooft, L

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review by Dhiman, P, Ma, J, Andaur Navarro, CL, Speich, B, Bullock, G, Damen, JAA, Hooft, L, Kirtley, S, Riley, RD, Van Calster, B, Moons, KGM, Collins, GS

Risk of bias of prognostic models developed using machine learning: a systematic review in oncology by Dhiman, P, Ma, J, Andaur Navarro, CL, Speich, B, Bullock, G, Damen, JAA, Hooft, L, Kirtley, S, Riley, RD, Van Calster, B, Moons, KGM, Collins, GS

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review by Dhiman, P, Ma, J, Andaur Navarro, CL, Speich, B, Bullock, G, Damen, JAA, Hooft, L, Kirtley, S, Riley, RD, Van Calster, B, Moons, KGM, Collins, GS

Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed by Levis, B, Snell, KIE, Damen, JAA, Hattle, M, Ensor, J, Dhiman, P, Andaur Navarro, CL, Takwoingi, Y, Whiting, PF, Debray, TPA, Reitsma, JB, Moons, KGM, Collins, GS, Riley, RD