The Incidence of Cancer in a Population‐Based Cohort of Canadian Heart Transplant Recipients
Bibliographic record
Abstract
To assess the long-term risk of developing cancer among heart transplant recipients compared to the Canadian general population, we carried out a retrospective cohort study of 1703 patients who received a heart transplant between 1981 and 1998, identified from the Canadian Organ Replacement Register database. Vital status and cancer incidence were determined through record linkage to the Canadian Mortality Database and Canadian Cancer Registry. Cancer incidence rates among heart transplant patients were compared to those of the general population. The observed number of incident cancers was 160 with 58.9 expected in the general population (SIR = 2.7, 95% CI = 2.3, 3.2). The highest ratios were for non-Hodgkin's lymphoma (NHL) (SIR = 22.7, 95% CI = 17.3, 29.3), oral cancer (SIR = 4.3, 95% CI = 2.1, 8.0) and lung cancer (SIR = 2.0, 95% CI = 1.2, 3.0). Compared to the general population, SIRs for NHL were particularly elevated in the first year posttransplant during more recent calendar periods, and among younger patients. Within the heart transplant cohort, overall cancer risks increased with age, and the 15-year cumulative incidence of all cancers was estimated to be 17%. There is an excess of incident cases of cancer among heart transplant recipients. The relative excesses are most marked for NHL, oral and lung cancer.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".