Nasal continuous positive airway pressure versus nasal intermittent positive pressure ventilation for preterm neonates: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: To determine whether nasal intermittent positive pressure ventilation (NIPPV) is more effective in preterm infants than nasal continuous positive airway pressure (NCPAP) in reducing the rate of extubation failure following mechanical ventilation, and reducing the frequency of apnoea of prematurity and subsequent need for endotracheal intubation. METHODS: Randomized trials of NIPPV versus NCPAP were sought and their data extracted and analysed independently by the authors using the methodology of the Cochrane Collaboration. The analysis used relative risk (RR), risk difference (RD) and number needed to treat (NNT) with 95% confidence intervals. RESULTS: The three studies identified, comparing NIPPV with NCPAP in the postextubation period, all used synchronized NIPPV (SNIPPV), which was more effective than NCPAP in preventing failure of extubation [RR 0.21 (0.10, 0.45), RD -0.32 (-0.45, -0.20), NNT 3 (2, 5)]. Two studies compared NIPPV versus NCPAP for the treatment of apnoea of prematurity. Although meta-analysis was not possible one trial showed a reduction in apnoea frequency with NIPPV and the other a trend favouring NIPPV. CONCLUSION: SNIPPV is an effective method of augmenting the beneficial effects of NCPAP in preterm infants in the postextubation period. Further research is required to delineate the role of NIPPV in the management of apnoea of prematurity.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.028 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".