The Future of Surfactant Therapy during ALI/ARDS
Bibliographic record
Abstract
The importance of pulmonary surfactant in maintaining normal lung function, and the observations that alterations in endogenous surfactant contribute to the lung dysfunction associated with acute lung injury (ALI) and the acute respiratory distress syndrome (ARDS), provide a rationale for administering exogenous surfactant in this setting. The results of clinical trials have been variable, however, in part due to the various surfactant preparations used, the different delivery and dosing methods employed, and the types of patients targeted for this therapy. Based on the insight gained from these studies, ongoing trials have modified these factors to optimize outcome, including one trial that is focusing on patients with direct lung insults such as pneumonia and aspiration. The future of surfactant therapy may well take advantage of the recently described host defense functions of this material. Based on extensive in vitro data as well as in vivo animal studies demonstrating the anti-inflammatory and antibacterial functions of various surfactant components, administration of surfactant earlier in the course of the disease, when lung inflammation is present but before severe lung dysfunction occurs, may prove to be optimal. This review discusses both the biophysical and host defense functions of surfactant in the context of novel therapeutic approaches for patients with ALI/ARDS.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".