Cannabis use is associated with reduced access to kidney transplantation and an increased risk of acute rejection post‐transplant
Bibliographic record
Abstract
BACKGROUND: The association between cannabis use and access to waitlisting, transplantation, and post-transplant outcomes remains uncertain. METHODS: Patients referred for kidney transplant (KT) to the University Health Network from January 1, 2003, to June 30, 2020, and followed until December 31, 2020, were included. Predictors of reported cannabis use were examined using a logistic regression model. The association between cannabis use and time to clearance for KT, undergoing KT, and post-transplant outcomes was evaluated using Cox proportional hazards models. RESULTS: Among 3734 patients, the prevalence of reported cannabis use was 11.8%. Cannabis use was associated with a lower likelihood of KT clearance (adjusted hazard ratio [aHR] .82 [95% confidence interval (CI): .72, .94]). Once cleared for KT, cannabis use did not predict the subsequent receipt of KT (aHR .92, [95% CI: .79, 1.08]). Among 2091 KT recipients, cannabis use was associated with a higher likelihood of biopsy-proven acute rejection (aHR 1.55, [95% CI: 1.06, 2.27]). The relative hazard of death-censored graft failure was similarly elevated (aHR 1.60 [95% CI: .95, 2.72]). Cannabis use did not predict total graft failure (aHR 1.33 [95% CI: .90, 1.96]), death with graft function (aHR 1.06 [95% CI: .59, 1.89]), or hospital readmission in the first-year post-transplant (aHR 1.26 [95% CI: .95, 1.68]). CONCLUSIONS: Cannabis users have less access to transplantation and an increased risk of acute rejection, possibly leading to more graft loss. Further studies are warranted to understand possible mechanisms for the increased risk of allograft immune injury among cannabis users.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".