Acute Lung Injury in Children—Kids Really Aren’t Just “Little Adults”
Bibliographic record
Abstract
OBJECTIVE: To describe the planned aims and methodology of the Pediatric Acute Lung Injury Consensus Conference. DESIGN: Consensus conference of experts in pediatric acute lung injury. METHODS: A panel of 26 experts in pediatric acute lung injury will meet over the course of one year to develop a better taxonomy to define pediatric acute lung injury, specifically predisposing factors, etiology, and pathophysiology. A modified Delphi approach that emphasizes strong professional agreement will be utilized. RESULTS: The Pediatric Acute Lung Injury Consensus Conference will aim for consensus development on the following topics related to pediatric acute lung injury: 1) definition, incidence, and epidemiology; 2) comorbidities and severity; 3) ventilatory support; 4) pulmonary-specific ancillary treatment; 5) nonpulmonary treatment; 6) monitoring; 7) noninvasive support and ventilation; 8) extracorporeal support; and 9) morbidity and long-term outcomes. CONCLUSIONS: The importance of this effort for improving care and guiding future research in pediatric acute lung injury is clear. Despite the many epidemiologic, interventional, and outcome studies undertaken by pediatric intensivists worldwide, our understanding of this disease process is limited, and morbidity and mortality remain unacceptably high. By consolidating the knowledge and expertise of the leaders of the field of pediatric acute lung injury, we hope to develop a framework for future progress.
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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 | 0.048 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".