Critical care and ventilatory management of deceased organ donors impact lung use and recipient graft survival
Bibliographic record
Abstract
Current risk-adjusted models for donor lung use and lung graft survival do not include donor critical care data. We sought to identify modifiable donor physiologic and mechanical ventilation parameters that predict donor lung use and lung graft survival. This is a prospective observational study of donors after brain death (DBDs) managed by 19 Organ Procurement Organizations from 2016 to 2019. Demographics, mechanical ventilation parameters, and critical care data were recorded at standardized time points during donor management. The lungs were transplanted from 1811 (30%) of 6052 DBDs. Achieving ≥7 critical care endpoints was a positive predictor of donor lung use. After controlling for recipient factors, donor blood pH positively predicted lung graft survival (OR 1.48 per 0.1 unit increase in pH) and the administration of dopamine during donor management negatively predicted lung graft survival (OR 0.19). Tidal volumes ≤8 ml/kg predicted body weight (OR 0.65), and higher positive end-expiratory pressures (OR 0.91 per cm H 2 O) predicted decreased donor lung use without affecting lung graft survival. A randomized clinical trial is needed to inform optimal ventilator management strategies in DBDs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".