Dyspnoea and respiratory muscle ultrasound to predict extubation failure
Bibliographic record
Abstract
Background This study investigated dyspnoea intensity and respiratory muscle ultrasound early after extubation to predict extubation failure. Methods The study was conducted prospectively in two intensive care units in France and Canada. Patients intubated for at least 48 h were studied within 2 h after an extubation following a successful spontaneous breathing trial. Dyspnoea was evaluated by a dyspnoea visual analogue scale (Dyspnoea-VAS) ranging from 0 to 10 and the Intensive Care Respiratory Distress Observational Scale (IC-RDOS). The ultrasound thickening fraction of the parasternal intercostal and the diaphragm was measured; limb muscle strength was evaluated using the Medical Research Council (MRC) score (range 0–60). Results Extubation failure occurred in 21 out of 122 enrolled patients (17%). The median (interquartile range (IQR)) Dyspnoea-VAS and IC-RDOS were higher in patients with extubation failureversussuccess: 7 (4–9)versus3 (1–5) (p<0.001) and 3.7 (1.8–5.8)versus1.7 (1.5–2.1) (p<0.001), respectively. The median (IQR) ratio of parasternal intercostal muscle to diaphragm thickening fraction was significantly higher and MRC was lower in patients with extubation failure compared with extubation success: 0.9 (0.4–2.1)versus0.3 (0.2–0.5) (p<0.001) and 45 (36–50)versus52 (44–60) (p=0.012), respectively. The thickening fraction of the parasternal intercostal and its ratio to diaphragm thickening showed the highest area under the receiver operating characteristic curve (AUC) for an early prediction of extubation failure (0.81). AUCs of Dyspnoea-VAS and IC-RDOS reached 0.78 and 0.74, respectively. Conclusions Respiratory muscle ultrasound and dyspnoea measured within 2 h after extubation predict subsequent extubation failure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".