Extubation Success Prediction in a Multicentric Cohort of Patients with Severe Brain Injury
Bibliographic record
Abstract
BACKGROUND: Patients with brain injury are at high risk of extubation failure. METHODS: We conducted a prospective observational cohort study in four intensive care units of three university hospitals. The aim of the study was to create a score that could predict extubation success in patients with brain injury. RESULTS: A total of 437 consecutive patients with brain injury were included, and 338 patients (77.3%) displayed successful extubation. In the multivariate analysis, four features were associated with success the day of extubation: age less than 40 yr, visual pursuit, swallowing attempts, and a Glasgow coma score greater than 10. In the score, each item counted as one. A score of 3 or greater was associated with 90% extubation success. The area under the receiver-operator curve was 0.75 (95% CI, 0.69 to 0.81). After internal validation by bootstrap, the area under the receiver-operator curve was 0.73 (95% CI, 0.68 to 0.79). Extubation success was significantly associated with shorter duration of mechanical ventilation (11 [95% CI, 5 to 17 days] vs. 22 days [95% CI, 13 to 29 days]; P < 0.0001), shorter intensive care unit length of stay (15 [95% CI, 9 to 23 days] vs. 27 days [95% CI, 21 to 36 days]; P < 0.0001), and lower in-intensive care unit mortality (4 [1.2%] vs. 11 [11.1%]; P < 0.0001). CONCLUSIONS: Our score exploring both airway functions and neurologic status may increase the probability of successful extubation in patients with severe brain injury.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".