Predictors of mortality in acute lung injury during the era of lung protective ventilation
Bibliographic record
Abstract
BACKGROUND: Lung protective ventilation has been widely adopted for the management of acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). Consequently, ventilator associated lung injury and mortality have decreased. It is not known if this ventilation strategy changes the prognostic value of previously identified demographic and pulmonary predictors of mortality, such as respiratory compliance and the arterial oxygen tension to inspired oxygen fraction ratio (Pao(2)/Fio(2)). METHODS: Demographic, clinical, laboratory and pulmonary variables were recorded in 149 patients with ALI/ARDS. Significant predictors of mortality were identified in bivariate analysis and these were entered into multivariate analysis to identify independent predictors of mortality. RESULTS: Hospital mortality was 41%. In the bivariate analysis, 17 variables were significantly correlated with mortality, including age, APACHE II score and the presence of cirrhosis. Pulmonary parameters associated with death included Pao(2)/Fio(2) and oxygenation index ((mean airway pressurexFio(2)x100)/Pao(2)). In unadjusted analysis, the odds ratio (OR) of death for Pao(2)/Fio(2) was 1.57 (CI 1.12 to 3.04) per standard deviation decrease. However, in adjusted analysis, Pao(2)/Fio(2) was not a statistically significant predictor of death, with an OR of 1.29 (CI 0.82 to 2.02). In contrast, oxygenation index (OI) was a statistically significant predictor of death in both unadjusted analysis (OR 1.89 (CI 1.28 to 2.78)) and in adjusted analysis (OR 1.84 (CI 1.13 to 2.99)). CONCLUSIONS: In this cohort of patients with ALI/ARDS, OI was an independent predictor of mortality, whereas Pao(2)/Fio(2) was not. OI may be a superior predictor because it integrates both airway pressure and oxygenation into a single variable.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".