Critically reading machine learning literature in neurosurgery: a reader’s guide and checklist for appraising prediction models

Neurosurgical FOCUS

DOI: 10.3171/2023.3.focus2352 Full text (OA) OpenAlex

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Calculating the sample size required for developing a clinical prediction model
2020 BMJ article Mathematics Statistical Methods in Epidemiology Open access

2,902

citations
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
2021 BMJ Open article Decision Sciences Meta-analysis and systematic reviews Open access cited by 1 patent

844

citations
Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury
2020 Journal of Clinical Epidemiology article Medicine Traumatic Brain Injury and Neurovascular Disturbances Open access

198

citations
Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease
2022 European Heart Journal article Medicine Artificial Intelligence in Healthcare and Education Open access

139

citations
Machine learning in neurosurgery: a global survey
2020 Acta Neurochirurgica article Medicine Artificial Intelligence in Healthcare and Education Open access

78

citations
Development and External Validation of a Machine Learning Model for the Early Prediction of Doses of Harmful Intracranial Pressure in Patients with Severe Traumatic Brain Injury
2022 Journal of Neurotrauma article Medicine Traumatic Brain Injury and Neurovascular Disturbances Open access

42

citations
Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery
2020 Journal of neurosurgery article Medicine Glioma Diagnosis and Treatment Open access

23

citations

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