Continuous positive airway pressure causes lung injury in a model of sepsis
Bibliographic record
Abstract
Continuous positive airway pressure, aimed at preventing pulmonary atelectasis, has been used for decades to reduce lung injury in critically ill patients. In neonatal practice, it is increasingly used worldwide as a primary form of respiratory support due to its low cost and because it reduces the need for endotracheal intubation and conventional mechanical ventilation. We studied the anesthetized in vivo rat and determined the optimal circuit design for delivery of continuous positive airway pressure. We investigated the effects of continuous positive airway pressure following lipopolysaccharide administration in the anesthetized rat. Whereas neither continuous positive airway pressure nor lipopolysaccharide alone caused lung injury, continuous positive airway pressure applied following intravenous lipopolysaccharide resulted in increased microvascular permeability, elevated cytokine protein and mRNA production, and impaired static compliance. A dose-response relationship was demonstrated whereby higher levels of continuous positive airway pressure (up to 6 cmH(2)O) caused greater lung injury. Lung injury was attenuated by pretreatment with dexamethasone. These data demonstrate that despite optimal circuit design, continuous positive airway pressure causes significant lung injury (proportional to the airway pressure) in the setting of circulating lipopolysaccharide. Although we would currently avoid direct extrapolation of these findings to clinical practice, we believe that in the context of increasing clinical use, these data are grounds for concern and warrant further investigation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".