Minimizing misclassification bias with a model to identify acetabular fractures using health administrative data: A cohort study

Acetabular fractures (AFs) are relatively uncommon thereby limiting their study. Analyses using population-based health administrative data can return erroneous results if case identification is inaccurate (‘misclassification bias’). This study measured the impact of an AF prediction model based exc...

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Detalles Bibliográficos
Autores principales: Adamczyk, Andrew, Grammatopoulos, George, van Walraven, Carl
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2021
Materias:
3700
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8718247/
https://www.ncbi.nlm.nih.gov/pubmed/34967356
http://dx.doi.org/10.1097/MD.0000000000028223

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8718247/
https://www.ncbi.nlm.nih.gov/pubmed/34967356
http://dx.doi.org/10.1097/MD.0000000000028223

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