Bibliographic record
Abstract
Background and aims: The MRC (Medical Research Council) grading for muscle strength is widely recommended as a reliable screening tool for ICU-AW in the adult ICU population. However, its use in the pediatric ICU (PICU) population has not been adequately evaluated. Aims: The objective of this study was to evaluate the feasibility and inter-rater reliability of using MRC in critically ill children. Methods: Prospective cohort, IRB approved study. Children aged 1–18 years, limited to bed-rest with a PICU stay of at least 48 hours were eligible. Two clinically trained independent raters evaluated MRCs on children from the time of enrollment and then weekly until hospital discharge. Raters were blinded to each others’ assessments. Results: 33 patients consented and were enrolled. 21/33 (64%) patients had at least one completed MRC exam, while MRCs could not be completed in 12 (36%). 55 of a total of 95 MRC attempts were not completed, most commonly because of patient sedation, hemodynamic instability, patient refusal and inability to cooperative due to developmental age. The median time to first successfully completed MRC exam was 7 days (0–51). Agreement between raters on MRC grading was poor (ICC 0.0, agreement was 33% for distal muscle groups, 48% for proximal, and 29% for the sum total MRC score respectively). Conclusions: There are multiple limitations to the use of MRC in critically ill children. MRC grading is not a reliable nor feasible method of screening for muscle weakness in the PICU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.521 | 0.358 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".