Nonimmunologic Donor-Recipient Pairing, HLA Matching, and Graft Loss in Deceased Donor Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND: In kidney transplantation, nonimmunologic donor-recipient (D-R) pairing is generally not given the same consideration as immunologic matching. The aim of this study was to determine how nonimmunologic D-R pairing relates to independent donor and recipient factors, and to immunologic HLA match for predicting graft loss. METHODS: Seven D-R pairings (race, sex, age, weight, height, cytomegalovirus serostatus, and HLA match) were assessed for their association with the composite outcome of death or kidney graft loss using a Cox regression-based forward stepwise selection model. The best model for predicting graft loss (including nonimmunologic D-R pairings, independent D-R factors, and/or HLA match status) was determined using the Akaike Information Criterion. RESULTS: Twenty three thousand two hundred sixty two (29.9%) people in the derivation data set and 9892 (29.7%) in the validation data set developed the composite outcome of death or graft loss. A model that included both independent and D-R pairing variables best predicted graft loss. The c-indices for the derivation and validation models were 0.626 and 0.629, respectively. Size mismatch (MM) between donor and recipient (>30 kg [D < R} and >15 cm [D < R]) was associated with poor patient and graft survival even with 0 HLA MM, and conversely, an optimal D-R size pairing mitigated the risk of graft loss seen with 6 HLA MM. CONCLUSIONS: D-R pairing is valuable in predicting patient and graft outcomes after kidney transplant. D-R size matching could offset the benefit and harm seen with 0 and 6 HLA MM, respectively. This is a novel finding.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".