Incidence and Outcomes of Pediatric Acute Lung Injury
Bibliographic record
Abstract
OBJECTIVE: This population-based, prospective, cohort study was designed to determine the population incidence and outcomes of pediatric acute lung injury. METHODS: Between 1999 and 2000, 1 year of screening was performed at all hospitals admitting critically ill children in King County, Washington. County residents 0.5 to 15 years of age who required invasive (through endotracheal tube or tracheostomy) or noninvasive (through full face mask) mechanical ventilation, regardless of the duration of mechanical ventilation, were screened. From this population, children meeting North American-European Consensus Conference acute lung injury criteria were eligible for enrollment. Postoperative patients who received mechanical ventilation for <24 hours were excluded. Data collected included the presence of predefined cardiac conditions, demographic and physiological data, duration of mechanical ventilation, and deaths. US Census population figures were used to estimate incidence. Associations between outcomes and subgroups identified a priori were assessed. RESULTS: Thirty-nine children met the criteria for acute lung injury, resulting in a calculated incidence of 12.8 cases per 100000 person-years. Severe sepsis (with pneumonia as the infection focus) was the most common risk factor. The median 24-hour Pediatric Risk of Mortality III score was 9.0, and the mean +/- SD was 11.7 +/- 7.5. The hospital mortality rate was 18%, lower than that reported previously for pediatric acute lung injury. There were no statistically significant associations between age, gender, or risk factors and outcomes. CONCLUSIONS: We present the first population-based estimate of pediatric acute lung injury incidence in the United States. Population incidence and mortality rates are lower than those for adult acute lung injury. Low mortality rates in pediatric acute lung injury may necessitate clinical trial outcome measures other than death.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".