Inhaled Nitric Oxide in Preterm Infants: An Individual-Patient Data Meta-analysis of Randomized Trials
Bibliographic record
Abstract
BACKGROUND: Inhaled nitric oxide (iNO) is an effective therapy for pulmonary hypertension and hypoxic respiratory failure in term infants. Fourteen randomized controlled trials (n = 3430 infants) have been conducted on preterm infants at risk for chronic lung disease (CLD). The study results seem contradictory. DESIGN/METHODS: Individual-patient data meta-analysis included randomized controlled trials of preterm infants (<37 weeks' gestation). Outcomes were adjusted for trial differences and correlation between siblings. RESULTS: Data from 3298 infants in 12 trials (96%) were analyzed. There was no statistically significant effect of iNO on death or CLD (59% vs 61%: relative risk [RR]: 0.96 [95% confidence interval (CI): 0.92-1.01]; P = .11) or severe neurologic events on imaging (25% vs 23%: RR: 1.12 [95% CI: 0.98-1.28]; P = .09). There were no statistically significant differences in iNO effect according to any of the patient-level characteristics tested. In trials that used a starting iNO dose of >5 vs ≤ 5 ppm there was evidence of improved outcome (interaction P = .02); however, these differences were not observed at other levels of exposure to iNO. This result was driven primarily by 1 trial, which also differed according to overall dose, duration, timing, and indication for treatment; a significant reduction in death or CLD (RR: 0.85 [95% CI: 0.74-0.98]) was found. CONCLUSIONS: Routine use of iNO for treatment of respiratory failure in preterm infants cannot be recommended. The use of a higher starting dose might be associated with improved outcome, but because there were differences in the designs of these trials, it requires further examination.
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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 | 0.029 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".