Oxygenation Index Predicts Outcome in Children with Acute Hypoxemic Respiratory Failure
Bibliographic record
Abstract
To define outcome and time dependence of predictors of outcome in pediatric acute hypoxemic respiratory failure, 131 patients (age range, 1 month to 18 years) were prospectively followed. Parametric models were used to describe time-related events, and competing risks analysis was performed for mortality estimates. Multiple logistic analysis was applied to describe time-related predictors of ventilation time and mortality. Overall mortality was 27%. Peak oxygenation index (OI) measured at any time point (p < 0.001, 91% reliability in bootstrapping, after inverse transformation) and Pediatric Risk of Mortality, or PRISM, score within the first 12 hours of mechanical ventilation (p < 0.001, 63% reliability in bootstrapping, after square transformation) were identified as independent predictors of mortality. Peak OI, younger age, and need for renal replacement therapy were significantly associated with a longer time to extubation. Although OI was less reliable as outcome predictor within the first 12 hours of intubation, it still predicted duration of mechanical ventilation. No clear-cut threshold of OI was identified that could accurately predict mortality. Survival was characterized by a peak rate of extubations at approximately 1 week, with a more gradual decline thereafter, whereas death appeared as a constant risk over time, which exceeded chances of survival at approximately 4 weeks. Severity of oxygenation failure at any point in time during acute hypoxemic respiratory failure correlates with duration of mechanical ventilation and mortality. This is best reflected by the OI, which shows a direct correlation to outcome in a time-independent manner.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".