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ABSTRACT: Background
Clinicians recognise that some critically ill children are difficult-to-sedate. It may be possible to identify this clinical phenotype for sedation response using statistical modelling techniques adopted from machine learning. This requires identification of a finite number of variables to include in the statistical model.Objective
To establish face and content validity for 17 candidate variables identified in the international literature as characteristic of the difficult-to-sedate child phenotype.Methods
Paediatric critical care clinicians rated the relevance of 17 variables characterising the difficult-to-sedate child using a four-point scale ranging from not (1) to highly relevant (4). Face and content validity of these variables were assessed by calc
SUBMITTER: Lebet RM
PROVIDER: S-EPMC5936660 | biostudies-literature | 2018 May
REPOSITORIES: biostudies-literature