Limiting Overdistention or Collapse When Mechanically Ventilating Injured Lungs: A Randomized Study in a Porcine Model
Bibliographic record
Abstract
Abstract Rationale It is unknown whether preventing overdistention or collapse is more important when titrating positive end-expiratory pressure (PEEP) in acute respiratory distress syndrome (ARDS). Objectives To compare PEEP targeting minimal overdistention or minimal collapse or using a compromise between collapse and overdistention in a randomized trial and to assess the impact on respiratory mechanics, gas exchange, inflammation, and hemodynamics. Methods In a porcine model of ARDS, lung collapse and overdistention were estimated using electrical impedance tomography during a decremental PEEP titration. Pigs were randomized to three groups and ventilated for 12 hours: PEEP set at ≤3% of overdistention (low overdistention), ≤3% of collapse (low collapse), and the crossing point of collapse and overdistention. Measurements and Main Results Thirty-six pigs (12 per group) were included. Median (interquartile range) values of PEEP were 7 (6–8), 11 (10–11), and 15 (12–16) cm H2O in the three groups (P < 0.001). With low overdistension, 6 (50%) pigs died, whereas survival was 100% in both other groups. Cause of death was hemodynamic in nature, with high transpulmonary vascular gradient and high epinephrine requirements. Compared with the other groups, pigs surviving with low overdistension had worse respiratory mechanics and gas exchange during the entire protocol. Minimal differences existed between crossing-point and low-collapse animals in physiological parameters, but postmortem alveolar density was more homogeneous in the crossing-point group. Inflammatory markers were not significantly different. Conclusions PEEP to minimize overdistention resulted in high mortality in an animal model of ARDS. Minimizing collapse or choosing a compromise between collapse and overdistention may result in less lung injury, with potential benefits of the compromise approach.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".