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Systematic Review Finds “Spin” Practices and Poor Reporting Standards in Studies on Machine Learning-based Prediction Models by Constanza L. Andaur Navarro; Johanna A.A. Damen; Toshihiko Takada; Steven W.J. Nijman; Paula Dhiman; Jie Ma; Gary S. Collins; Ram Bajpai; Richard D. Riley; Karel G.M. Moons; Lotty Hooft is a Decision Sciences article available to read on EtoBox.

What is Systematic Review Finds “Spin” Practices and Poor Reporting Standards in Studies on Machine Learning-based Prediction Models about?

Objective We evaluated the presence and frequency of spin practices and poor reporting standards in studies that developed and/or validated clinical prediction models using supervised machine learning techniques. Study Design and Setting We systematically searched PubMed from 01-2018 to 12-2019 to identify diagnostic and prognostic prediction model studies using supervised machine learning. No restrictions were placed on data source, outcome, or clinical specialty. Results We included 152 studies: 38% reported diagnostic models and 62% prognostic models. When reported, discrimination was described without precision estimates in 53/71 abstracts (74.6%, [95% CI 63.4 - 83.3]) and 53/81 main texts (65.4%, [95% CI 54.6 - 74.9]). Of the 21 abstracts that recommended the model to be used in daily practice, 20 (95.2% [95% CI 77.3 - 99.8]) lacked any external validation of the developed models. Likewise, 74/133 (55.6% [95% CI 47.2 - 63.8]) studies made recommendations for clinical use in their main text without any external validation. Reporting guidelines were cited in 13/152 (8.6% [95% CI 5.1 - 14.1]) studies. Conclusion Spin practices and poor reporting standards are also present in stud

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Author

Constanza L. Andaur Navarro; Johanna A.A. Damen; Toshihiko Takada; Steven W.J. Nijman; Paula Dhiman; Jie Ma; Gary S. Collins; Ram Bajpai; Richard D. Riley; Karel G.M. Moons; Lotty Hooft

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Decision Sciences (Social Sciences)