Heated Humidified High-Flow Nasal Cannula for Preterm Infants: An Updated Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Heated humidified high-flow nasal cannula (HHHFNC) is gaining popularity as a mode of respiratory support. We updated a systematic review and meta-analyses examining the efficacy and safety of HHHFNC compared with standard treatments for preterm infants. The primary outcome was the need for reintubation for preterm infants following mechanical ventilation (post-extubation analysis) or need for intubation for preterm infants not previously intubated (analysis of primary respiratory support). METHODS: We searched PubMed, MEDLINE, Embase, and the Cochrane Library for randomized controlled trials (RCTs) of HHHFNC versus standard treatments. Meta-analysis was conducted using Review Manager 5.3. RESULTS: The post-extubation analysis included ten RCTs (n = 1,201), and the analysis of primary respiratory support included ten RCTs (n = 1,676). There were no statistically significant differences for outcomes measuring efficacy, including the primary outcome. There were statistically significant differences favoring HHHFNC versus nasal cannula positive airway pressure (NCPAP) for air leak (post-extubation, risk ratio [RR] 0.29, 95 percent confidence interval [CI] 0.11 to 0.76, I2 = 0) and nasal trauma (post-extubation: 0.35, 95 percent CI 0.27 to 0.46, I2 = 5 percent; primary respiratory support: RR 0.52, 95 percent CI 0.37 to 0.74; I2 = 27 percent). Studies, particularly those of primary respiratory support, included very few preterm infants with gestational age (GA) <28 weeks. CONCLUSIONS: HHHFNC may offer an efficacious and safe alternative to NCPAP for some infants but evidence is lacking for preterm infants with GA ≤28 weeks.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".