Immunosuppression and cancer risk in kidney transplant recipients: A retrospective cohort study
Bibliographic record
Abstract
We assessed whether contemporary immunosuppression agents were associated with cancer among kidney transplant recipients (KTR), and if this association varied by age and sex. We studied a retrospective province-wide cohort of primary KTR (1997-2016). Employing multivariable Cox models, we estimated associations of cumulative doses of prednisone, mycophenolate and tacrolimus administered over the past 10 years, lagged by 2 years, with the incidence of primary malignant neoplasms (PMN). We assessed interactions with age and sex. To assess the impact of exposure recency, we used weighted cumulative exposure (WCE) modeling. Among 1064 KTR, 108 (10.2%) developed PMN over median follow-up of 73 months (interquartile range: 32-120). Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) of 0.96 (0.64-1.43), 1.34 (0.96-1.86), and 1.06 (0.88-1.29) were estimated for cumulative daily doses of prednisone (5 mg), mycophenolate (1000 mg), and tacrolimus (2 mg) administered continuously over the past 10 years, respectively. PMN risk associated with cumulative tacrolimus exposure was modified by age (interaction p = .035) and was more pronounced in 15-year and 30-year-old KTR (aHRs of 1.57 [1.08-2.28] and 1.31 [1.03-1.66], respectively) in comparison to older KTR. PMN risk increase associated with higher cumulative mycophenolate dose was more pronounced in females (aHR = 1.86 [1.15-3.00]) than in males (aHR = 1.16 [0.74-1.81]; interaction p = .131). WCE analyses suggested increased PMN risk the higher the mycophenolate doses taken 5-10 years ago. A trend toward increased PMN risk with long-term mycophenolate exposure, particularly in females, and more pronounced risk with long-term tacrolimus exposure in younger KTR, identify opportunities for tailored immunosuppression to mitigate cancer risk.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".