Lung Transplantation With Donation After Circulatory Determination of Death Donors and the Impact of Ex Vivo Lung Perfusion
Bibliographic record
Abstract
The growing demand for suitable lungs for transplantation drives the quest for alternative strategies to expand the donor pool. The aim of this study is to evaluate the outcomes of lung transplantation (LTx) with donation after circulatory determination of death (DCDD) and the impact of selective ex vivo lung perfusion (EVLP). From 2007 to 2013, 673 LTx were performed, with 62 (9.2%) of them using DCDDs (seven bridged cases). Cases bridged with mechanical ventilation/extracorporeal life support were excluded. From 55 DCDDs, 28 (51%) underwent EVLP. Outcomes for LTx using DCDDs and donation after neurological determination of death (DNDD) donors were similar, with 1 and 5-year survivals of 85% and 54% versus 86% and 62%, respectively (p = 0.43). Although comparison of survival curves between DCDD + EVLP versus DCDD-no EVLP showed no significant difference, DCDD + EVLP cases presented shorter hospital stay (median 18 vs. 23 days, p = 0.047) and a trend toward shorter length of mechanical ventilation (2 vs. 3 days, p = 0.059). DCDDs represent a valuable source of lungs for transplantation, providing similar results to DNDDs. EVLP seems an important technique in the armamentarium to safely increase lung utilization from DCDDs; however, further studies are necessary to better define the role of EVLP in this context.
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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 | 0.004 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".