Changes in Positive End-Expiratory Pressure Alter the Distribution of Ventilation within the Lung Immediately after Birth in Newborn Rabbits
Bibliographic record
Abstract
Current recommendations suggest the use of positive end-expiratory pressures (PEEP) to assist very preterm infants to develop a functional residual capacity (FRC) and establish gas exchange at birth. However, maintaining a consistent PEEP is difficult and so the lungs are exposed to changing distending pressures after birth, which can affect respiratory function. Our aim was to determine how changing PEEP levels alters the distribution of ventilation within the lung. Preterm rabbit pups (28 days gestation) were delivered and mechanically ventilated with one of three strategies, whereby PEEP was changed in sequence; 0-5-10-5-0 cmH2O, 5-10-0-5-0 cmH2O or 10-5-0-10-0 cmH2O. Phase contrast X-ray imaging was used to analyse the distribution of ventilation in the upper left (UL), upper right (UR), lower left (LL) and lower right (LR) quadrants of the lung. Initiating ventilation with 10PEEP resulted in a uniform increase in FRC throughout the lung whereas initiating ventilation with 5PEEP or 0PEEP preferentially aerated the UR than both lower quadrants (p<0.05). Consequently, the relative distribution of incoming VT was preferentially directed into the lower lobes at low PEEP, primarily due to the loss of FRC in those lobes. Following ventilation at 10PEEP, the distribution of air at end-inflation was uniform across all quadrants and remained so regardless of the PEEP level. Uniform distribution of ventilation can be achieved by initiating ventilation with a high PEEP. After the lungs have aerated, small and stepped reductions in PEEP result in more uniform changes in ventilation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".