Noninvasive respiratory support in neonates: a brief review.
Bibliographic record
Abstract
In the era of gentle ventilation and open lung strategy noninvasive ventilatory support in neonates has gained momentum and its use in nurseries around the world is also increased. This paper reviews various modalities of non-invasive respiratory support in some details and its relevance in the recent evidence based use. Continuous positive airway pressure (CPAP) is a mode of ventilatory assistance in which positive pressure is delivered to the airway throughout the respiratory cycle. It is also referred to as continuous distending pressure (CDP) or positive end expiratory pressure (PEEP) when applied through a ventilator along with intermittent mandatory ventilation (IMV). It has been proven over the years to be an effective mode of ventilatory support and as such has gained widespread use in the management of a variety of neonatal respiratory diseases. It is relatively cheap and easy to apply and certainly feasible for routine use in underdeveloped world. Besides improving oxygenation CPAP often functions as an airway stabilizer of the trachea thus helping to decrease the frequency of neonatal apneas, particularly the obstructive variety. There is good to fair quality supportive evidence from several studies that the use of primary CPAP can reduce the need for intubation and mechanical ventilation in infants less than 32 weeks gestation. In this review, we will attempt to describe different delivery devices and pressure generating systems and discuss different ways in which CPAP can be applied. Although it is unclear that primary use of CPAP can reduce overall neonatal mortality and morbidity it is becoming increasingly clear that early CPAP use is less invasive, baby friendly and decreases the need and frequency of the use of surfactants. Besides, clinical indications for CPAP, its advantages and limitations will also be explored. CPAP adjuncts such as nasal intermittent positive pressure ventilation (NIPPV) and infant flow driver will also be discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".