Risk of cancer among adult solid organ transplant recipients in Quebec, Canada: 1997–2016
Bibliographic record
Abstract
IMPORTANCE: Solid organ transplant (SOT) recipients have a 2-3 times higher cancer risk due to immunosuppressive therapy used for organ rejection, but Canadian data are limited. Understanding cancer incidence in this population is crucial for improving screening and preventive strategies. OBJECTIVE: To estimate cancer incidence among Quebec SOT recipients and compare their risk with that of the general population. SETTING AND DESIGN: We linked two provincial administrative databases from 1997 to 2016 to conduct a retrospective cohort study. Cancer incidence rates were stratified by sex and age, and standardized risk ratios and 95% confidence intervals were calculated by comparing the observed cancer cases in our study population to the expected cases in the general population using the Quebec cancer registry. PARTICIPANTS: A total of 6,873 transplant recipients, including 4,284 kidney, 1,142 liver, 612 heart, 443 lung, and 392 other/multiple transplant recipients. MAIN OUTCOME AND MEASURES: The primary outcome of interest was cancer incidence. Cancer incidence rates were calculated per 1,000 person-years. The standardized risk ratio (SRR) was used to quantify cancer risk relative to the general population. RESULTS: Among 6,873 transplant recipients, 1,142 developed cancers, yielding an incidence rate of 23.5 per 1000 person-years (95% CI: 22.1-24.9). Skin cancer was the most common, followed by lymphoid, hematopoietic, and digestive cancers. The overall SRR showed a 2.6-fold higher cancer risk than in the general population. CONCLUSION AND RELEVANCE: Solid organ transplant recipients in Quebec face a higher cancer risk than the general population. A nationwide study is needed to inform health policies and improve management of this vulnerable population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".