Subjective Assessment of Motor Function by the Bedside Nurses in Mechanically Ventilated Surgical Intensive Care Unit Patients Predicts Tracheostomy
Bibliographic record
Abstract
OBJECTIVE: In many institutions, intensive care unit (ICU) nurses assess their patients' muscle function as part of their routine bedside examination. We tested the research hypothesis that this subjective examination of muscle function prior to extubation predicts tracheostomy requirement. METHODS: Adult, mechanically ventilated patients admitted to 7 ICUs at Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2019 were included in this observational study. Assessment of motor function was performed every four hours by ICU nurses. Multivariable logistic regression analysis controlled for acute disease severity, delirium risk assessment through the confusion assessment method for the ICU (CAM-ICU), and pre-defined predictors of extubation failure was applied to examine the association of motor function and tracheostomy within 30 days after extubation. RESULTS: Within 30 days after extubation, 891 of 9609 (9.3%) included patients required a tracheostomy. The inability to spontaneously move and hold extremities against gravity within 24 h prior to extubation was associated with significantly higher odds of 30-day tracheostomy (adjusted OR 1.56, 95% CI 1.27-1.91, p < 0.001, adjusted absolute risk difference (aARD) 2.8% (p < 0.001)). The effect was magnified among patients who were mechanically ventilated for >7 days (aARD 21.8%, 95% CI 12.4-31.2%, p-for-interaction = 0.015). CONCLUSIONS: ICU nurses' subjective assessment of motor function is associated with 30-day tracheostomy risk, independent of known risk factors. Muscle function measurements by nursing staff in the ICU should be discussed during interprofessional rounds.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".