Ex Vivo Perfusion Treatment of Infection in Human Donor Lungs
Bibliographic record
Abstract
Ex vivo lung perfusion (EVLP) is a platform to treat infected donor lungs with antibiotic therapy before lung transplantation. Human donor lungs that were rejected for transplantation because of clinical concern regarding infection were randomly assigned to two groups. In the antibiotic group (n = 8), lungs underwent EVLP for 12 h with high-dose antibiotics (ciprofloxacin 400 mg or azithromycin 500 mg, vancomycin 15 mg/kg, and meropenem 2 g). In the control group (n = 7), lungs underwent EVLP for 12 h without antibiotics. A quantitative decrease in bacterial counts in bronchoalveolar lavage (BAL) was found in all antibiotic-treated cases but in only two control cases. Perfusate endotoxin levels at 12 h were significantly lower in the antibiotic group compared with the control group. EVLP with broad-spectrum antibiotic therapy significantly improved pulmonary oxygenation and compliance and reduced pulmonary vascular resistance. Perfusate endotoxin levels at 12 h were strongly correlated with levels of perfusates tumor necrosis factor α, IL-1β and macrophage inflammatory proteins 1α and 1β at 12 h. In conclusion, EVLP treatment of infected donor lungs with broad-spectrum antibiotics significantly reduced BAL bacterial counts and endotoxin levels and improved donor lung function.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".