La perfusion pulmonaire ex-vivo : un atout pour l’appariement donneur-receveur des patients hyperimmunises en transplantation pulmonaire
Bibliographic record
Abstract
Objectives: Nowadays, prospective crossmatch is routinely performed for matching donor and recipient in kidney transplantation. Transplantation is decided only in case of a negative result. The practice leads us to think that a hyperimmunized lung transplantation candidate should also get a negative prospective crossmatch result before transplantation. We implemented a strategy for transplanting hyperimmunized patients using ”logistic” ex-vivo lung perfusion (EVLP) in order to safely extend preservation time while waiting for the prospective result of the crossmatch. Methods: We used EVLP as described by the Toronto Lung Transplantation team for 3 hyperimmunized patients at Foch Hospital. Results: Patients were 29, 47, and 49 years old, respectively. Two patients were awaiting lung transplantation for cystic fibrosis and one patient for emphysema. Mean EVLP length was 3 hours and 40 minutes with a total preservation time of 14 hours. Two patients had bilateral lung transplantation and one patient bilateral lobar transplantation. One patient was extubated in the operating room, while two others were extubated 5 and 7 days after transplantation, respectively. The mean ICU and hospital lengths of stay were 12 days and 25 days, respectively. During follow-up, no argument for antibody-mediated rejection or chronic allograft dysfunction was detected for the 3 patients. C4d was systematically negative. All patients are alive and well 3 years after transplantation. Conclusion: These 3 cases highlight “logistic” EVLP as suitable and safe for allowing lung transplantation in hyperimmunized patients.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".