Recombinant Surfactant Protein C–based Surfactant for Patients with Severe Direct Lung Injury
Bibliographic record
Abstract
RATIONALE: Patients with acute lung injury have impaired function of the lung surfactant system. Prior clinical trials have shown that treatment with exogenous recombinant surfactant protein C (rSP-C)-based surfactant results in improvement in blood oxygenation and have suggested that treatment of patients with severe direct lung injury may decrease mortality. OBJECTIVES: Determine the clinical benefit of administering an rSP-C-based synthetic surfactant to patients with severe direct lung injury due to pneumonia or aspiration. METHODS: A prospective randomized blinded study was performed at 161 centers in 22 countries. Patients were randomly allocated to receive usual care plus up to eight doses of rSP-C surfactant administered over 96 hours (n = 419) or only usual care (n = 424). MEASUREMENTS AND MAIN RESULTS: Mortality to 28 days after treatment, the requirement for mechanical ventilation, and the number of nonpulmonary organ failure-free days were not different between study groups. In contrast to prior studies, there was no improvement in oxygenation in patients receiving surfactant compared with the usual care group. Investigation of the possible reasons underlying the lack of efficacy suggested a partial inactivation of rSP-C surfactant caused by a step of the resuspension process that was introduced with this study. CONCLUSIONS: In this study, rSP-C-based surfactant was of no clinical benefit to patients with severe direct lung injury. The unexpected lack of improvement in oxygenation, coupled with the results of in vitro tests, suggest that the administered suspension may have had insufficient surface activity to achieve clinical benefit.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".