Evaluation of Exogenous Surfactant in HCl-induced Lung Injury
Bibliographic record
Abstract
The efficacy of exogenous surfactant administration is influenced by numerous factors, which has resulted in variable outcomes of clinical trials evaluating this treatment for the acute respiratory distress syndrome (ARDS). We investigated several of these factors in an animal model of acid aspiration including different surfactant preparations, and different delivery methods. In addition, high-frequency oscillation (HFO), a mode of mechanical ventilation known to recruit severely damaged lungs, was utilized. Lung injury was induced in adult rabbits via intratracheal instillation of 0.2 N HCl followed by conventional mechanical ventilation (CMV) until Pa(O2)/FI(O2) values ranged from 220 to 270 mm Hg. Subsequently, animals were given one of three surfactants administered via three different methods and physiological responses were assessed over a 1-h period. Regardless of the surfactant treatment strategy utilized, oxygenation responses were not sustained. In contrast, HFO resulted in a superior response compared with all surfactant treatment strategies involving CMV. The deterioration in physiological parameters after surfactant treatment was likely due to overwhelming protein inhibition of the surfactant. In conclusion, various surfactant treatment strategies were not effective in this model of lung injury, although the lungs of these animals were recruitable with HFO, as reflected by the acute and sustained oxygenation improvements.
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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".