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Softw."},{"key":"10.1016\/j.neucom.2024.128767_b92","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/978-3-030-16399-0_5","article-title":"Overfitting and optimism in prediction models","author":"Steyerberg","year":"2019","journal-title":"Clin. Predict. Models: a Practical Approach Develop. Validat. Updating"},{"key":"10.1016\/j.neucom.2024.128767_b93","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106874","article-title":"Medical deep learning\u2014A systematic meta-review","volume":"221","author":"Egger","year":"2022","journal-title":"Comput. 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