The incidence, management, and evolution of rapamycin‐related side effects in kidney transplant recipients
Bibliographic record
Abstract
Conversion from a calcineurin-inhibitor-based immunosuppression to a rapamycin-based immunosuppression may preserve kidney graft function. The side effects of rapamycin can limit its usefulness, but their management and evolution are rarely reported in clinical trials. We performed a retrospective cohort study in patients transplanted before December 31, 2008 and who received rapamycin to replace calcineurin inhibitors. In 219 patients studied, 98% presented ≥1 side effects after starting rapamycin. Side effects occurring in ≥10% of patients were dyslipidemia (52%, 95% confidence interval (CI): 45-59%), peripheral edema (37%, 95%CI: 31-43%), cytopenia (36%, 95% CI: 30-42%), acne (29%, 95% CI: 23-35%), proteinuria (23%, 95% CI: 17-29%), and oral ulcers 14% (95% CI: 10-18%). Proteinuria, ulcers, and edema were difficult to manage and were more likely to cause cessation of rapamycin. Rapamycin was discontinued in 46% of patients (95% CI: 40-52%). Age (odds ratio [OR] per 10-yr increase: 1.29, 95% CI: 1.05-1.59) and obesity (OR: 2.57, 95% CI: 1.10-6.01) were independently associated with cessation of rapamycin. We conclude that successful control of dyslipidemia and cytopenia can be achieved without discontinuing rapamycin. Most other side effects are harder to manage. Leaner and younger patients are less likely to discontinue rapamycin due to side effects.
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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 | 0.006 |
| 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 | 0.001 |
| 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".