Spontaneous Breathing Improves Lung Aeration in Oleic Acid–induced Lung Injury
Bibliographic record
Abstract
BACKGROUND: Experimental and clinical studies have shown reduction in intrapulmonary shunt with improved oxygenation by spontaneous breathing with airway pressure release ventilation (APRV) in acute lung injury. The mechanisms of these findings are not clear. The authors hypothesized that spontaneous breathing results in better aeration of lung tissue and that improvement in oxygenation can be explained by these changes. This hypothesis was studied in a porcine model of oleic acid-induced lung injury. METHODS: Two hours after induction of lung injury, 24 pigs were randomly assigned to APRV with or without spontaneous breathing at a positive end-expiratory pressure of 5 cm H(2)O. Hemodynamics, spirometry, and end-expiratory lung volume by nitrogen washout were measured at baseline, after 2 h of lung injury, and after 2 and 4 h of mechanical ventilation in the specific mode. Finally, spiral computed tomography of the chest was performed at end-expiratory lung volume in 22 pigs. RESULTS: Arterial carbon dioxide tension and mean and end-inspiratory airway pressures were comparable between settings. Four hours of APRV with spontaneous breathing resulted in improved oxygenation compared with APRV without spontaneous breathing (arterial oxygen tension, 144 +/- 65 vs. 91 +/- 50 mmHg, P < 0.01 for interaction time x mode), higher end-expiratory lung volume (786 +/- 320 vs. 384 +/- 148 ml, P < 0.001), and better aeration. End-expiratory lung volume and venous admixture were both correlated with the amount of lung reaeration (r(2) = 0.62 and r(2) = 0.61, respectively). CONCLUSIONS: The results support the hypothesis that spontaneous breathing during APRV improves oxygenation mainly by recruitment of nonaerated lung and improved aeration of the lungs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".