Feasibility and Reliability of Muscle Strength Testing in Critically Ill Children
Bibliographic record
Abstract
Diagnosing pediatric intensive care unit-acquired weakness (PICU-AW) is challenging. The Medical Research Council (MRC) score is a widely used screening method for muscle weakness in critically ill adults; however, its utility in critically ill children has not been established. Our objective was to determine the feasibility and interobserver reliability of muscle strength testing using MRC score in critically ill children. A prospective observational substudy of critically ill children aged 1 to 17 years and limited to bed rest during the first 48 hours of PICU admission was evaluated with weekly MRC exams independently performed by two clinical raters. MRC exams were attempted on all 33 participants, but could be completed in only 21 (64%), 9 of who (43%) received at least one exam while in the PICU, and in the remaining 12 (57%), MRC exams could only be completed after PICU discharge. Of the 95 attempted MRC exams, 55 (57%) could not be conducted or completed, most commonly due to patient sedation, and inability to comply due to cognitive ability, pain, or noncooperation. The inter-rater reliability for MRC sum score was excellent (intraclass correlation coefficient: 0.87). However, the inter-rater reliability was only moderate when used to determine PICU-AW (Cohen kappa: 0.48). MRC testing in the PICU was not feasible as an early screening tool for muscle weakness in the majority of critically ill children in this study. Further research is needed to find an appropriate screening tool that is both feasible and predicts clinically relevant outcomes in children, such as function and recovery following critical illness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".