Optimizing lung aeration at birth using a sustained inflation and positive pressure ventilation in preterm rabbits
Bibliographic record
Abstract
BACKGROUND: A sustained inflation (SI) facilitates lung aeration, but the most effective pressure and duration are unknown. We investigated the effect of gestational age (GA) and airway liquid volume on the required inflation pressure and SI duration. METHODS: Rabbit kittens were delivered at 27, 29, and 30 d gestation, intubated and airway liquid was aspirated. Either no liquid (control) or 30 ml/kg of liquid was returned to the airways. Lung gas volumes were measured by plethysmography and phase-contrast X-ray-imaging. Starting at 22 cmH2O, airway pressure was increased until airflow commenced and pressure was then held constant. The SI was truncated when 20 ml/kg air had entered the lung and ventilation continued with intermittent positive pressure ventilation (iPPV). RESULTS: Higher SI pressures and longer durations were required in 27-d kittens compared to 30-d kittens. During iPPV, 27-d kittens needed higher pressures and had lower functional residual capacity (FRC) compared to 30-d kittens. Adding lung liquid increased SI duration, reduced FRC, and increased resistance and pressures during iPPV in 29- and 30-d kittens. CONCLUSION: Immature kittens required higher starting pressures and longer SI durations to achieve a set inflation volume. Larger airway liquid volumes adversely affected lung function during iPPV in older but not young kittens.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".