Early national trends of lung allograft use during donation after circulatory death heart procurement in the United States
Bibliographic record
Abstract
Objective: Innovative technology such as normothermic regional perfusion and the Organ Care System has expanded donation after circulatory death heart transplantation. We wanted to investigate the impact of donation after circulatory death heart procurement in concurrent lung donation and implantation at a national level. Methods: We reviewed the United Network for Organ Sharing database for heart donation between December 2019 and March 2022. Donation after circulatory death donors were separated from donation after brain death donors and further categorized based on concomitant organ procurement of lung and heart, or heart only. Results: < .001). Of the 121 lungs from 64 donation after circulatory death donors, 22 lungs were retrieved but discarded (32.2%). Normothermic regional perfusion was performed in 37.3% of donation after circulatory death donors, and there was no difference in lung use between normothermic regional perfusion versus direct procurement and perfusion (20.2% and 18.8%). There was also no difference in 1-year survival between normothermic regional perfusion and direct procurement and perfusion. Conclusions: Although national use of donation after circulatory death hearts has increased, donation after circulatory death lungs has remained at a steady state. The implantation of lungs after concurrent procurement with the heart remains low, whereas transplantation of donation after circulatory death hearts is greater than 90%. The use of normothermic regional perfusion lungs has been controversial, and we report comparable 1-year outcomes to standard donation after circulatory death lungs. Further studies are warranted to investigate the underlying mechanisms of normothermic regional perfusion on 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".