Decline in Ventilatory Ratio as a Predictor of Mortality in Adults With ARDS Receiving Prone Positioning
Bibliographic record
Abstract
BACKGROUND: Prone positioning reduces mortality in patients with moderate/severe ARDS. It remains unclear which physiological parameters could guide clinicians to assess which patients are likely to benefit from prone position. This study aimed to determine the association between relative changes in physiological parameters at 24 h of prone positioning and ICU mortality in adult subjects with ARDS. METHODS: We conducted a cohort study using the VENTILA database, including adults with ARDS receiving prone positioning. We used multivariable logistic regression to assess the association between relative changes in physiological parameters (P aO 2 / F IO 2 , dynamic driving pressure, P aCO 2 , and ventilatory ratio defined as [minute ventilation [mL/min] × P aCO 2 [mm Hg]]/[predicted body weight × 100 [mL/min] × 37.5 [mm Hg] with ICU mortality) (primary outcome). We report adjusted odds ratios with 95% CI as measures of association. RESULTS: We included 156 subjects of which 82 (53%) died in the ICU. A relative decline in the ventilatory ratio at 24 h was associated with lower ICU mortality (odds ratio 0.80 [95% CI 0.66–0.97], every 10% decrease). Relative changes in P aO 2 /F IO 2 (odds ratio 0.89 [95% CI 0.77–1.03], every 25% increase), P aCO 2 (odds ratio 0.97 [95% CI 0.82–1.16], every 10% decrease), and dynamic driving pressure (odds ratio 0.98 [95% CI 0.89–1.07], every 10% decrease) were not associated with ICU mortality. CONCLUSIONS: In subjects with ARDS receiving prone positioning, a relative decline in the ventilatory ratio at 24 h was associated with lower ICU mortality.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".