Polyethylene Glycol (PEG) Attenuates Exogenous Surfactant in Lung-injured Adult Rabbits
Bibliographic record
Abstract
Exogenous surfactant administration in patients with the acute respiratory distress syndrome is currently being evaluated, although resource limitations and the potential expense are existing concerns. Previous in vitro and in vivo studies have shown that substances such as polyethylene glycol (PEG) added to exogenous surfactant improved the function of the surfactant. Based on these data, we hypothesized that PEG would augment surfactant function in an adult rabbit model of lung injury induced by lung lavage and mechanical ventilation, and that this would be accomplished by altering surfactant metabolism. Contrary to our hypothesis, however, mean Pa(O(2)), Pa(CO(2)), and peak inspiratory pressures values 3 h after treatment were significantly worse in the surfactant + PEG treatment groups compared with the surfactant alone groups. These effects were observed for two different doses of surfactant tested. Lavage analyses after sacrifice showed that animals given PEG with their surfactant had significantly lower total and large aggregate surfactant pool sizes compared with animals given surfactant alone. We conclude that in this lung injury model, PEG attenuated surfactant responses, suggesting that further preclinical studies are required before testing this approach in humans.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".