Non-Immunologic Pairing of Kidney Transplant Donors and Recipients
Bibliographic record
Abstract
Currently, allocation preference in deceased donor kidney transplantation is given to donors and recipients (D/R) with 0 human leukocyte antigen (HLA) mismatches (MM), with non-immunologic matching between D/R rarely considered. Thus, the primary aim for this study was to determine if suboptimal pairing at five D/R loci (age, race, sex, weight and CMV serostatus) results in an increased risk of kidney graft failure, how this impact relates to HLA MM, and if HLA MM modifies this effect. The association of D/R match with death-censored graft loss was explored using deceased donor recipients from 2000-2014 identified through the Scientific Registry of Transplants Recipients. A Cox regression coefficient-based scoring system was used to assign points to each D/R pairing. Points were totaled across all five D/R loci and overall match was categorized as favorable (<0), moderate (0 to < 8), or poor (≥8), with cut points chosen to create relatively even tertiles. D/R match groups were stratified by HLA match (favorable (0MM) or poor (1-6MM)). Time to graft loss was evaluated using multivariable Cox Proportional Hazards models adjusting for known literature predictors of graft loss. The interaction between HLA MM and D/R pairing was assessed to determine if HLA MM modifies the association between D/R pairing and graft outcome. 17,786 of 107,041 kidney transplant recipients developed death-censored graft failure. D/R matching was a stronger predictor of graft failure than HLA MM. The highest HRs for graft failure were observed for poorly matched D/R, irrespective of HLA MM (HR 3.50 95% CI [3.18-3.85]; HR 2.73 95% CI [2.40-3.11] for poor and favorably matched HLA, respectively). Even with suboptimal HLA match, the risk of graft loss was mitigated for those with a favorable D/R match (HR 1.51 95% CI [1.37-1.66]). Both D/R match and HLA MM were independently associated with the outcome of interest and and the interaction between D/R match and HLA MM was significant. In conclusion, non-immunologic D/R matching is a stronger predictor of death-censored graft failure than is HLA match status. HLA MM modifies this association.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".