Acute lung injury in children: Therapeutic practice and feasibility of international clinical trials*
Bibliographic record
Abstract
OBJECTIVES: To describe mechanical ventilation strategies in acute lung injury and to estimate the number of eligible patients for clinical trials on mechanical ventilation management. In contrast to adult medicine, there are few clinical trials to guide mechanical ventilation management in children with acute lung injury. DESIGN: A cross-sectional study for six 24-hr periods from June to November 2007. SETTING: Fifty-nine pediatric intensive care units in 12 countries in North America and Europe. PATIENTS: We identified children meeting acute lung injury criteria and collected detailed information on illness severity, mechanical ventilatory support, and use of adjunctive therapies. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 3823 patients screened, 414 (10.8%) were diagnosed with acute lung injury by their treating physician, but only 165 (4.3%) patients met prestablished inclusion/exclusion criteria to this trial and, therefore, would have been eligible for a clinical trial. Of these, 124 (75.2%) received conventional mechanical ventilation, 27 (16.4%) received high-frequency oscillatory ventilation, and 14 (8.5%) received noninvasive mechanical ventilation. In the conventional mechanical ventilation group, 43.5% were ventilated in a pressure control mode with a mean tidal volume of 8.3 ± 3.3 mL/kg; and there was no clear relationship between positive end-expiratory pressure and Fio2 delivery in the conventional mechanical ventilation group. Use of adjunctive treatments, including nitric oxide, prone positioning, surfactant, hemofiltration, recruitment maneuvers, steroids, bronchodilators, and fluid restriction, was highly variable. CONCLUSIONS: Our study reveals inconsistent mechanical ventilation practice and use of adjunctive therapies in children with acute lung injury. Pediatric clinical trials assessing mechanical ventilation management are needed to generate evidence to optimize outcomes. We estimate that a large number of centers (∼60) are needed to conduct such trials; it is imperative, therefore, to bring about international collaboration.
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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 | 0.469 | 0.617 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".