Sonometric assessment of cough predicts extubation failure: SonoWean—a proof-of-concept study
Bibliographic record
Abstract
Abstract Background Extubation failure is associated with increased mortality. Cough ineffectiveness may be associated with extubation failure, but its quantification for patients undergoing weaning from invasive mechanical ventilation (IMV) remains challenging. Methods Patients under IMV for more than 24 h completing a successful spontaneous T-tube breathing trial (SBT) were included. At the end of the SBT, we performed quantitative sonometric assessment of three successive coughing efforts using a sonometer. The mean of the 3-cough volume in decibels was named Sonoscore. Results During a 1-year period, 106 patients were included. Median age was 65 [51–75] years, mainly men (60%). Main reasons for IMV were acute respiratory failure (43%), coma (25%) and shock (17%). Median duration of IMV at enrollment was 4 [3–7] days. Extubation failure occurred in 15 (14%) patients. Baseline characteristics were similar between success and failure extubation groups, except percentage of simple weaning which was lower and MV duration which was longer in extubation failure patients. Sonoscore was significantly lower in patients who failed extubation (58 [52–64] vs. 75 [70–78] dB, P < 0.001). After adjustment on MV duration and comorbidities, Sonoscore remained associated with extubation failure. Sonoscore was predictive of extubation failure with an area under the ROC curve of 0.91 (IC 95% [0.83–0.99], P < 0.001). A threshold of Sonoscore < 67.1 dB predicted extubation failure with a sensitivity of 0.93 IC 95% [0.70–0.99] and a specificity of 0.82 IC 95% [0.73–0.90]. Conclusion Sonometric assessment of cough strength might be helpful to identify patients at risk of extubation failure in patients undergoing IMV.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".