262 * INCIDENCE AND SEVERITY OF PRIMARY GRAFT DYSFUNCTION AFTER LUNG TRANSPLANTATION USING REJECTED GRAFTS RECONDITIONED WITH EX VIVO LUNG PERFUSION
Bibliographic record
Abstract
Objectives: Ex vivo lung perfusion (EVLP) is a novel technique used to evaluate and recondition marginal or rejected grafts. Primary graft dysfunction (PGD) is a major early complication after lung transplantation (LTx). The use of marginal or initially rejected grafts may increase its incidence and severity. The aim of this study is to evaluate the incidence of PGD after LTx using rejected grafts reconditioned with EVLP. Methods: PGD has been evaluated immediately after LTx (T0) and after 72 hours (T72) in lung transplanted patients receiving standard (group A) or reconditioned (group B) grafts. EVLP was performed using a controlled acellular perfusion according to the Toronto technique. Results: From July 2011 to February 2013, 36 lung transplants have been performed: 28 patients (21 M/7 F, mean age 51.7 ± 14.7 years) in group A and eight patients (6 M/2 F, mean age 46.6 ± 9.8 years) in group B. The incidence of PGD at T0 was (group A vs group B): PGD 0-1, 29% vs 63%; PGD 2, 21% vs 0%; PGD 3, 50% vs 37%. PGD at T72 was: PGD 0-1, 54% vs 88%; PGD 2, 21% vs 12%; PGD 3, 25% vs 0%. Extracorporeal membrane oxygenation was required in four patients in group A and in two patients in group B. Conclusions: The use of initially rejected grafts treated with EVLP does not increase the incidence and severity of PGD after LTx. Although comparison of PGD 3 incidence in the two groups did not reach a statistical difference, all EVLP patients suffering from severe PGD early after transplant recovered a normal lung function at 72 hours, suggesting a protective role of EVLP on PGD occurrence and severity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".