Johanna AAG Damen

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

2021 · 332 Zit. · BMJ

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

2025 · 175 Zit. · BMJ

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

2022 · 124 Zit. · BMC Medical Research Methodology

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

2022 · 119 Zit. · BMC Medical Research Methodology

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

2022 · 89 Zit. · Journal of Clinical Epidemiology

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

2020 · 84 Zit. · BMJ Open

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

2022 · 61 Zit. · Diagnostic and Prognostic Research

Systematic review finds “spin” practices and poor reporting standards in studies on machine learning-based prediction models

2023 · 56 Zit. · Journal of Clinical Epidemiology

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

2023 · 36 Zit. · Journal of Clinical Epidemiology

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

2021 · 14 Zit.

SPIN-PM: a consensus framework to evaluate the presence of spin in studies on prediction models

2024 · 8 Zit. · Journal of Clinical Epidemiology

Methodological quality assessment tools for diagnosis and prognosis research: overview and guidance

2024 · 2 Zit. · Journal of Clinical Epidemiology