Neonatal Non-Invasive Respiratory Support: Synchronised NIPPV, Non-Synchronised NIPPV or Bi-Level CPAP: What Is the Evidence in 2013?
Bibliographic record
Abstract
Nasal continuous positive airway pressure (NCPAP) has proven to be an effective mode of non-invasive respiratory support in preterm infants; however, many infants still require endotracheal ventilation, placing them at an increased risk of morbidities such as bronchopulmonary dysplasia. Several other modes of non-invasive respiratory support beyond NCPAP, including synchronised and non-synchronised nasal intermittent positive pressure ventilation (SNIPPV and nsNIPPV) and bi-level positive airway pressure (BiPAP) are now also available. These techniques require different approaches, and the exact mechanisms by which they act remain unclear. SNIPPV has been shown to reduce the rate of reintubation in comparison to NCPAP when used as post-extubation support, but the evidence for nsNIPPV and BiPAP in this context is less convincing. There is some evidence that NIPPV (whether synchronised or non-synchronised) used as primary respiratory support is beneficial, but the variation in study methodology makes this hard to translate confidently into clinical practice. There is currently no evidence to suggest a reduction in mortality or important morbidities such as bronchopulmonary dysplasia, with NIPPV or BiPAP in comparison to NCPAP, and there is a lack of appropriately designed studies in this area. This review discusses the different approaches and proposed mechanisms of action of SNIPPV, nsNIPPV and BiPAP, the challenges of applying the available evidence for these distinct modalities of non-invasive respiratory support to clinical practice, and possible areas of future research.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".