Positive Pressure Ventilation in Preterm Infants in the Delivery Room: A Review of Current Practices, Challenges, and Emerging Technologies
Bibliographic record
Abstract
BACKGROUND: A major proportion of preterm neonates require positive pressure ventilation (PPV) immediately after delivery. PPV may be administered through a face mask (FM) or nasal prongs. Current literature indicates that either of these are associated with similar outcomes. SUMMARY: Nonetheless, FM remains the most utilized and the best choice. However, most available FM sizes are too large for extremely preterm infants, which leads to mask leak and ineffective PPV. Challenges to providing effective PPV include poor respiratory drive, complaint chest wall, weak thoracic muscle, delayed liquid clearance, and surfactant deficiency in preterm infants. Mask leak, airway obstruction, poor technique, and inappropriate size are correctable causes of ineffective PPV. Visual assessment of chest rise is often used to assess the efficacy of PPV. However, its accuracy is debatable. Though end tidal CO2 may adjudge the effectiveness of PPV, clinical studies are limited. The compliance of a preterm lung is highly dynamic. The inflating pressure set on T-piece is constant throughout the resuscitation, but the lung volume and dynamics changes with every breath. This leads to huge fluctuations of tidal volume delivery and can trigger inflammatory cascade in preterm infants leading to brain and lung injury. Respiratory function monitoring in the delivery room has potential for guiding and optimizing delivery room resuscitation. This is, however, limited by high costs, complex information that is difficult to interpret during resuscitation, and absence of clinical trials. KEY MESSAGES: This review summarizes the existing literature on PPV in preterm infants, the various aspects related to it such as the pathophysiology, interfaces, devices utilized to deliver it, appropriate technique, emerging technologies, and future directions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".