Continuous Negative Abdominal Pressure Reduces Ventilator-induced Lung Injury in a Porcine Model
Bibliographic record
Abstract
BACKGROUND: In supine patients with acute respiratory distress syndrome, the lung typically partitions into regions of dorsal atelectasis and ventral aeration ("baby lung"). Positive airway pressure is often used to recruit atelectasis, but often overinflates ventral (already aerated) regions. A novel approach to selective recruitment of dorsal atelectasis is by "continuous negative abdominal pressure." METHODS: A randomized laboratory study was performed in anesthetized pigs. Lung injury was induced by surfactant lavage followed by 1 h of injurious mechanical ventilation. Randomization (five pigs in each group) was to positive end-expiratory pressure (PEEP) alone or PEEP with continuous negative abdominal pressure (-5 cm H2O via a plexiglass chamber enclosing hindlimbs, pelvis, and abdomen), followed by 4 h of injurious ventilation (high tidal volume, 20 ml/kg; low expiratory transpulmonary pressure, -3 cm H2O). The level of PEEP at the start was ≈7 (vs. ≈3) cm H2O in the PEEP (vs. PEEP plus continuous negative abdominal pressure) groups. Esophageal pressure, hemodynamics, and electrical impedance tomography were recorded, and injury determined by lung wet/dry weight ratio and interleukin-6 expression. RESULTS: All animals survived, but cardiac output was decreased in the PEEP group. Addition of continuous negative abdominal pressure to PEEP resulted in greater oxygenation (PaO2/fractional inspired oxygen 316 ± 134 vs. 80 ± 24 mmHg at 4 h, P = 0.005), compliance (14.2 ± 3.0 vs. 10.3 ± 2.2 ml/cm H2O, P = 0.049), and homogeneity of ventilation, with less pulmonary edema (≈10% less) and interleukin-6 expression (≈30% less). CONCLUSIONS: Continuous negative abdominal pressure added to PEEP reduces ventilator-induced lung injury in a pig model compared with PEEP alone, despite targeting identical expiratory transpulmonary pressure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".