Porcine Model of Donation after Cardiac Death (DCD) and Evaluation of Pulmonary Function Using Ex-vivo Lung Perfusion (EVLP)
Bibliographic record
Abstract
Objective The limiting factor when using lungs from donors after cardiac death (DCD) is the duration of warm ischemic time, which is linked to reperfusion edema. Within the context of lung shortages, and in order to avoid transplanting damaged lungs, ex-vivo lung perfusion (EVLP) has emerged as an innovative tool to preserve and recondition donor lungs. Using the EVLP technique in a porcine model, the purpose of this study is to determine the duration of warm ischemia that donor lungs can tolerate before they are viewed as non-viable for transplant. Methods This study is comprised of 5 groups (n=2-6/group). Four groups endured different warm ischemic times, whilst the fifth group underwent cold ischemia. The lungs were subsequently perfused outside the body using the EVLP platform for four hours. Results 120 minutes of warm ischemia is more damaging for lungs than 120 minutes of cold ischemia, even after being reconditioned on the EVLP platform (50,4 ± 8,9% vs. 3,3 ± 3,4% of weight gain). This would signify that two hours of warm ischemia is incompatible with transplantation. However, 90 minutes and 60 minutes of warm ischemia seems to have less of an effect on pulmonary functions. Indeed, the lungs of both these groups had less than 14% of weight gain and maintained oxygenating capacities (PaO2/FiO2 of 514 ± 12 and 586 ± 0 mmHg respectively.) Conclusion Lungs having been submitted to less than 90 minutes of warm ischemia and evaluated with EVLP may be suitable candidates for transplantation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".