Flaws in the development and validation of a covid-19 prediction model

Flaws in the development and validation of a covid-19 prediction model

التفاصيل البيبلوغرافية
المؤلفون الرئيسيون:	Collins, GS, Riley, RD, van Smeden, M
التنسيق:	Journal article
اللغة:	English
منشور في:	Oxford University Press 2020

مواد مشابهة

COVID-19 prediction models should adhere to methodological and reporting standards
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Flawed validation of FRAX
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Statistical flaws in the development of a prediction model.
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Stability of clinical prediction models developed using statistical or machine learning methods
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Flawed external validation study of the ADNEX model to diagnose ovarian cancer.
حسب: Van Calster, B, وآخرون
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Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review
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Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist
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Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration
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Methodology over metrics: current scientific standards are a disservice to patients and society
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Evaluation of clinical prediction models (part 2): how to undertake an external validation study
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Design flaws in EuroSCORE II.
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A flawed trial
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External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb
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Clinical prediction models and the multiverse of madness
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External validation of the PRIORITY model in predicting COVID-19 critical illness in vaccinated and unvaccinated patients
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Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study
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Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models
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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
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Statistical Primer: Developing and validating a risk prediction model
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SSL-TLS security flaws : the CRIME compression attack security flaws
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Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems?
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Flaws of the individual or the system?
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Flawed assets clauses
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Flaw growth and fracture
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