Coexistence and Impact of Limb Muscle and Diaphragm Weakness at Time of Liberation from Mechanical Ventilation in Medical Intensive Care Unit Patients
Bibliographic record
Abstract
RATIONALE: Intensive care unit (ICU)- and mechanical ventilation (MV)-acquired limb muscle and diaphragm dysfunction may both be associated with longer length of stay and worse outcome. Whether they are two aspects of the same entity or have a different prevalence and prognostic impact remains unclear. OBJECTIVES: To quantify the prevalence and coexistence of these two forms of ICU-acquired weakness and their impact on outcome. METHODS: O defined dysfunction) and ultrasonography (thickening fraction [TFdi] and excursion). Limb muscle weakness was defined as a Medical Research Council (MRC) score less than 48. MEASUREMENTS AND MAIN RESULTS: Seventy-six patients were assessed at their first spontaneous breathing trial: 63% had diaphragm dysfunction, 34% had limb muscle weakness, and 21% had both. There was a significant but weak correlation between MRC score and twitch pressure (ρ = 0.26; P = 0.03) and TFdi (ρ = 0.28; P = 0.01), respectively. Low twitch pressure (odds ratio, 0.60; 95% confidence interval, 0.45-0.79; P < 0.001) and TFdi (odds ratio, 0.84; 95% confidence interval, 0.76-0.92; P < 0.001) were independently associated with weaning failure, but the MRC score was not. Diaphragm dysfunction was associated with higher ICU and hospital mortality, and limb muscle weakness was associated with longer duration of MV and hospital stay. CONCLUSIONS: Diaphragm dysfunction is twice as frequent as limb muscle weakness and has a direct negative impact on weaning outcome. The two types of muscle weakness have only limited overlap.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".