Non-Invasive Ventilation in Neonatology
Bibliographic record
Abstract
BACKGROUND: Invasive mechanical ventilation (IMV) has been replaced by early continuous positive airway pressure (CPAP) in the treatment of respiratory distress syndrome (RDS) in preterm infants aiming to reduce the rate of bronchopulmonary dysplasia (BPD). Subsequently, modern non-invasive ventilation strategies (NIV) were introduced into clinical practice with limited evidence of effects on pulmonary and neurodevelopmental outcomes. METHODS: We performed a selective literature search in PubMed including randomized controlled trials (RCT) (n ≥ 200) and meta-analyses published in the field of NIV in neonatology and follow-up studies focusing on long term pulmonary and neurodevelopmental outcomes. RESULTS: Individual studies do not show a significant risk reduction for the combined endpoint death or BPD in preterm infants caused by early CPAP in RDS when compared to primary intubation. One meta-analysis comparing four studies found CPAP significantly reduces the risk of BPD or death (relative risk: 0.91; 95% confidence interval [0.84;0.99]). Nasal intermittent positive pressure ventilation (NIPPV) as a primary ventilation strategy reduces the rate of intubations in infants with RDS (RR: 0.78 [0.64;0.94]) when compared to CPAP but does not affect the rate of BPD (RR: 0.78 [0.58;1.06]). CONCLUSION: Early CPAP reduces the need for IMV and the risk of BPD or death in preterm infants with RDS. NIPPV may offer advantages over CPAP regarding intubation rates. Networking-based follow-up programs are required to assess the effect of NIV on long term pulmonary and neurodevelopmental outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".