Comprehensive outcomes after lung retransplantation: A single‐center review
Bibliographic record
Abstract
INTRODUCTION: Lung retransplantation is an important therapy for a growing population of lung transplant recipients with graft failure, but detailed outcome data are lacking. METHODS: We conducted a retrospective cohort study of adult lung retransplant in the Toronto Lung Transplant Program from 2001 to 2013 (n = 38). We analyzed the postoperative course, graft function, renal function, microbiology, donor-specific antibodies (DSA), quality of life, and survival compared to a control cohort of primary transplant recipients matched for age and era. RESULTS: Indication for retransplant was chronic lung allograft dysfunction in most retransplant recipients (35/38, 92%). The postoperative course was more complex after retransplant than primary (ventilation time, 8 vs 2 days, P < .01; ICU stay 14 vs 4 days, P < 0.01), and peak lung function was lower (FEV1 2.2L vs 3L, P < .01). Quality of life scores were comparable, as were renal function, microbiology, and donor-specific antibody formation. Median survival was 1988 days after primary and 1475 days after retransplant (P = .39). CONCLUSIONS: Lung retransplantation is associated with a more complex postoperative course and lower peak lung function, but the long-term medical profile is similar to primary transplant. Lung retransplantation can be beneficial for carefully selected candidates with allograft failure.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".