Cancer Diagnoses After Living Kidney Donation
Bibliographic record
Abstract
Background Mortality records identify cancer as the leading cause of death among living kidney donors, but information on the burden of cancer outside death records is limited in this population. Methods We examined a database wherein U.S. Organ Procurement and Transplantation Network identifiers for 4,650 living kidney donors in 1987 to 2007 were linked to administrative data of a U.S. private health insurer (2000–2007 claims) to identify postdonation cancer diagnoses. Skin cancer and non–skin cancer diagnoses were ascertained from International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes on billing claims. Donors were also matched one-to-one with general insurance beneficiaries by sex and age when benefits began. Diagnosis rates within observation windows were compared as rate ratios. Results The median time from donation to the end of plan insurance enrollment was 7.7 years, with a median observation period of 2.1 years. Skin cancer rates were similar among prior living donors in the observation period and nondonor controls (rate ratio, 0.91; 95% confidence interval [CI], 0.59–1.40). In contrast, the rate of total non–skin cancers was significantly less common among donors than among controls (rate ratio, 0.74; 95% CI, 0.55–0.99), although reduced relative risk was limited to donors captured earlier in relation to donation. Several cases of cancer diagnosis (uterine, melanoma, “other”) were identified within the first year after donation. Prostate cancer diagnosis was significantly more common among living donors compared with controls (rate ratio, 3.80; 95% CI, 1.42–10.2). Conclusions Continued study of cancer after kidney donation is warranted to ensure that evaluation, selection, and long-term follow-up support overall good health of the donor.
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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) | 0.002 | 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 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".