The impact of repeated mismatches in kidney transplantations performed after nonrenal solid organ transplantation
Bibliographic record
Abstract
The aim of this study was to determine whether kidney transplantations performed after previous nonrenal solid organ transplants are associated with worse graft survival when there are repeated HLA mismatches (RMM) with the previous donor(s). We performed a retrospective cohort study using data from the Scientific Registry of Transplant Recipients. Our cohort comprised 6624 kidney transplantations performed between January 1, 1990 and January 1, 2015. All patients had previously received 1 or more nonrenal solid organ transplants. RMM were observed in 35.3% of kidney transplantations and 3012 grafts were lost over a median follow-up of 5.4 years. In multivariate Cox regression analyses, we found no association between overall graft survival and either RMM in class 1 (hazard ratio [HR]: 0.97, 95% confidence interval [CI] 0.89-1.07) or class 2 (HR: 0.95, 95% CI 0.85-1.06). Results were similar for the associations between RMM, death-censored graft survival, and patient survival. Our results suggest that the presence of RMM with previous donor(s) does not have an important impact on allograft survival in kidney transplant recipients who have previously received a nonrenal solid organ transplant.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".