Liver transplantation and subsequent risk of cancer: Findings from a Canadian cohort study
Bibliographic record
Abstract
Characterization of the long-term cancer risks among liver transplant patients has been hampered by the paucity of sufficiently large cohorts. The increase over time in the number of liver transplants coupled with improved survival underscores the need to better understand associated long-term health effects. This is a cohort study whose subjects were assembled with data from the population-based Canadian Organ Replacement Registry. Analyses are based on 2034 patients who received a liver transplant between June 1983 and October 1998. Incident cases of cancer were identified through record linkage to the Canadian Cancer Registry. We compared site-specific cancer incidence rates in the cohort and the general Canadian population by using the standardized incidence ratio (SIR). Stratified analyses were performed to examine variations in risk according to age at transplantation, sex, time since transplantation, and year of transplantation. Liver transplant recipients had cancer incidence rates that were 2.5 times higher than those of the general population [95% confidence interval (CI) = 2.1, 3.0]. The highest SIR was observed for non-Hodgkin's lymphoma (SIR = 20.8, 95% CI = 14.9, 28.3), whereas a statistically significant excess was observed for colorectal cancer (SIR = 2.6, 95% CI = 1.4, 4.4). Risks were more pronounced during the first year of follow-up and among younger transplant patients. In conclusion, our findings indicate that liver transplant patients face increased risks of developing cancer with respect to the general population. Increased surveillance in this patient population, particularly in the first year following transplantation, and screening for colorectal cancer with modalities for which benefits are already well recognized should be pursued.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".