Reabsorption atelectasis in a porcine model of ARDS: regional and temporal effects of airway closure, oxygen, and distending pressure
Bibliographic record
Abstract
Little is known about the small airways dysfunction in acute respiratory distress syndrome (ARDS). By computed tomography (CT) imaging in a porcine experimental model of early ARDS, we aimed at studying the location and magnitude of peripheral airway closure and alveolar collapse under high and low distending pressures and high and low inspiratory oxygen fraction (FIO2). Six piglets were mechanically ventilated under anesthesia and muscle relaxation. Four animals underwent saline-washout lung injury, and two served as healthy controls. Beyond the site of assumed airway closure, gas was expected to be trapped in the injured lungs, promoting alveolar collapse. This was tested by ventilation with an FIO2 of 0.25 and 1 in sequence during low and high distending pressures. In the most dependent regions, the gas/tissue ratio of end-expiratory CT, after previous ventilation with FIO2 0.25 low-driving pressure, was significantly higher than after ventilation with FIO2 1; with high-driving pressure, this difference disappeared. Also, significant reduction in poorly aerated tissue and a correlated increase in nonaerated tissue in end-expiratory CT with FIO2 1 low-driving pressure were seen. When high-driving pressure was applied or after previous ventilation with FIO2 0.25 and low-driving pressure, this pattern disappeared. The findings suggest that low distending pressures produce widespread dependent airway closure and with high FIO2, subsequent absorption atelectasis. Low FIO2 prevented alveolar collapse during the study period because of slow absorption of gas behind closed airways.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".