Cancer Risk Following HLA-Incompatible Living Donor Kidney Transplantation
Bibliographic record
Abstract
Incompatible living donor kidney transplant recipients (ILDKTr) require desensitization to facilitate transplantation, and this substantial upfront immunosuppression may result in serious complications, including cancer. Methods: To characterize cancer risk in ILDKTr, we evaluated 858 ILDKTr and 12 239 compatible living donor kidney transplant recipients (CLDKTr) from a multicenter cohort with linkage to the US transplant registry and 33 cancer registries (1997-2016). Cancer incidence was compared using weighted Cox regression. Results: Among ILDKTr, the median follow-up time was 6.7 y (maximum 16.1 y) for invasive cancers (ascertained via cancer registry linkage) and 5.0 y (maximum 16.1 y) for basal and squamous cell carcinomas (ascertained via the transplant registry and censored for transplant center loss to follow-up). Invasive cancers occurred in 53 ILDKTr (6.2%) and 811 CLDKTr (6.6%; weighted hazard ratio [wHR] 1.01; 95% confidence interval [CI], 0.76-1.35). Basal and squamous cell carcinomas occurred in 41 ILDKTr (4.8%) and 737 CLDKTr (6.0%) (wHR 0.99; 95% CI, 0.69-1.40). Cancer risk did not vary according to donor-specific antibody strength, and in an exploratory analysis, was similar between CLDKTr and ILDKTr for most cancer types and according to cancer stage, except ILDKTr had a suggestively increased risk of colorectal cancer (wHR 3.27; 95% CI, 1.23-8.71); however, this elevation was not significant after correction for multiple comparisons. Conclusions: These findings indicate that the risk of cancer is not increased for ILDKTr compared with CLDKTr. The possible elevation in colorectal cancer risk is unexplained and might suggest a need for tailored screening or prevention.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".