Ventilation distribution during spontaneous breathing trials predicts liberation from mechanical ventilation: the VISION study
Bibliographic record
Abstract
BACKGROUND: Predicting complete liberation from mechanical ventilation (MV) is still challenging. Electrical impedance tomography (EIT) offers a non-invasive measure of regional ventilation distribution and could bring additional information. RESEARCH QUESTION: Whether the display of regional ventilation distribution during a Spontaneous Breathing Trial (SBT) could help at predicting early and successful liberation from MV. STUDY DESIGN AND METHODS: Patients were monitored with EIT during the SBT. The tidal image was divided into ventral and dorsal regions and displayed simultaneously. We explored the ventral-to-dorsal ventilation difference in percentage, and its association with clinical outcomes. Liberation success was defined pragmatically as passing SBT followed by extubation within 24 h without reintubation for 7 days. Failure included use of rescue therapy, reintubation within 7 days, tracheostomy, and not being extubated within 24 h after succesful SBT. A training cohort was used for discovery, followed by a validation cohort. RESULTS: Among a total of 98 patients analyzed, 85 passed SBT (87%), but rapid liberation success occurred only in 40; 13.5% of extubated patients required reintubation. From the first minutes to the entire SBT duration, the absolute ventral-to-dorsal difference was consistently smaller in liberation success compared to all subgroups of failure (p < 0.0001). An absolute difference at 5 min of SBT > 20% was associated with failure of liberation, with sensitivity and specificity of 71% and 78% and positive predictive value 81% in a validation cohort. CONCLUSION: During SBT, a large ventral-to-dorsal difference in ventilation indicated by EIT may help to rapidly identify patients at risk of liberation failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".