Bibliographic record
Abstract
OBJECTIVES: To outline both the preclinical and clinical data demonstrating surfactant alterations in acute lung injury, which provide the rationale for testing exogenous surfactant administration in this setting. We also review the results of the randomized, controlled clinical trials conducted to date that have evaluated this therapy in patients with acute respiratory distress syndrome, and we review the various factors that may have affected the outcomes of these trials. Future areas for surfactant research will also be addressed. DATA SYNTHESIS AND EXTRACTION: A review of the literature utilizing a MEDLINE search was performed using the key words: surfactant, surfactant administration, acute respiratory distress syndrome, and lung injury. Personal views are presented and references to unpublished clinical data are made based on the authors' access to this data. CONCLUSIONS: Exogenous surfactant administration has proven inconsistent as a therapeutic modality for patients with acute respiratory distress syndrome. This is because of the severity of the injury at the time of treatment and because of the variable surfactant preparations, dosing regimes, and delivery methods used in the different trials. Future research efforts will focus on determining the optimal timing of surfactant administration in patients at risk of developing acute respiratory distress syndrome with the aim of preventing progressive lung dysfunction and determining whether surfactant treatments need to be tailored to the specific patient in question. Moreover, with the recognition that surfactant also plays an important role in host defense, the future for surfactant therapy is exciting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".