Lung Transplantation from Donors after Circulatory Death Using Portable <i>Ex Vivo</i> Lung Perfusion
Bibliographic record
Abstract
BACKGROUND: Donation after circulatory death is a novel method of increasing the number of donor lungs available for transplantation. Using organs from donors after circulatory death has the potential to increase the number of transplants performed. METHODS: Three bilateral lung transplants from donors after circulatory death were performed over a six-month period. Following organ retrieval, all sets of lungs were placed on a portable ex vivo lung perfusion device for evaluation and preservation. RESULTS: Lung function remained stable during portable ex vivo perfusion, with improvement in partial pressure of oxygen/fraction of inspired oxygen ratios. Mechanical ventilation was discontinued within 48 h for each recipient and no patient stayed in the intensive care unit longer than eight days. There was no postgraft dysfunction at 72 h in two of the three recipients. Ninety-day mortality for all recipients was 0% and all maintain excellent forced expiratory volume in 1 s and forced vital capacity values post-transplantation. CONCLUSION: The authors report excellent results with their initial experience using donors after circulatory death after portable ex vivo lung perfusion. It is hoped this will allow for the most efficient use of available donor lungs, leading to more transplants and fewer deaths for potential recipients on wait lists.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".