Continuous Positive Airway Pressure versus Nasal Intermittent Positive Pressure Ventilation in Preterm Neonates: What if Mean Airway Pressures Were Equivalent?
Bibliographic record
Abstract
Respiratory support for preterm neonates in modern neonatal intensive care units is predominantly with the use of noninvasive interfaces. Continuous positive airway pressure (CPAP) and nasal intermittent positive pressure ventilation (NIPPV) are the prototypical and most commonly utilized forms of noninvasive respiratory support, and each has unique gas flow characteristics. In meta-analyses of clinical trials till date, NIPPV has been shown to likely reduce respiratory failure and need for intubation compared to CPAP. However, a significant limitation of the included studies has been the higher mean airway pressures used during NIPPV. Thus, it is unclear to what extent any benefits seen with NIPPV are due to the cyclic pressure application versus the higher mean airway pressures. In this review, we elaborate on these limitations and summarize the available evidence comparing NIPPV and CPAP at equivalent mean airway pressures. Finally, we call for further studies comparing noninvasive respiratory support modes at equal mean airway pressures. KEY POINTS: · Most current literature on CPAP vs. NIPPV in preterm neonates is confounded by use of higher mean airway pressures during NIPPV.. · In this review, we summarize existing evidence on CPAP vs. NIPPV at equivalent mean airway pressures.. · We call for future research on noninvasive support modes to account for mean airway pressures..
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".