Cancer Incidence Among Canadian Kidney Transplant Recipients
Bibliographic record
Abstract
A number of studies have observed increased cancer incidence rates among individuals who have received renal transplants. Generally, however, these studies have been limited by relatively small sample sizes, short follow-up intervals or focused on only one cancer site. We conducted a nationwide population-based study of 11,155 patients who underwent kidney transplantation between 1981 and 1998. Incident cancers were identified up to December 31, 1999, through record linkage to the Canadian Cancer Registry. Patterns of cancer incidence in the cohort were compared to the Canadian general population using standardized incidence ratios (SIRs). We examined variations in risk according time since transplantation, year of transplantation and age at transplantation. In our patient population, we observed a total of 778 incident cancers versus 313.2 expected (SIR = 2.5, 95% CI = 2.3-2.7). Site-specific SIRs were highest for cancer of the lip (SIR = 31.3, 95% CI = 23.5-40.8), non-Hodgkin's lymphoma (NHL) (SIR = 8.8, 95% CI = 7.4-10.5), and kidney cancer (SIR = 7.3, 95% CI = 5.7-9.2). SIRs for NHL and cancer of the lip and kidney were highest and among transplant patients. This study confirms previous findings of increased risks of posttransplant cancer. Our findings underscore the need for increased vigilance among kidney transplant recipients for cancers at sites where there are no population-based screening programs in place.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".