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

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

Bibliográfalaš dieđut
Váldodahkkit:	Collins, GS, Riley, RD, van Smeden, M
Materiálatiipa:	Journal article
Giella:	English
Almmustuhtton:	Oxford University Press 2020

Geahča maid

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Statistical flaws in the development of a prediction model.
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Flawed validation of FRAX
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Minimum sample size for developing a multivariable prediction model using multinomial logistic regression
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There are no shortcuts in the development and validation of a COVID‐19 prediction model
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Stability of clinical prediction models developed using statistical or machine learning methods
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Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review
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Methodology over metrics: current scientific standards are a disservice to patients and society
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Flawed external validation study of the ADNEX model to diagnose ovarian cancer.
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Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist
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Statistical issues in the development of COVID‐19 prediction models
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There is no such thing as a validated prediction model
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Cardiovascular vulnerability predicts hospitalisation in primary care clinically suspected and confirmed COVID-19 patients: A model development and validation study
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Design flaws in EuroSCORE II.
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FLAWED /
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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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Trustworthy or flawed clinical prediction rule?
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Clinical prediction models and the multiverse of madness
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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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Statistical flaws of the fitness-fatigue sports performance prediction model
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Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review
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Born to fail: flaws in replication design produce intended results
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Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems?
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Risk, Trust, and Flawed Assumptions: Vaccine Hesitancy During the COVID-19 Pandemic
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A flawed trial
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