Five-Year Study of Tacrolimus as Secondary Intervention Versus Continuation of Cyclosporine in Renal Transplant Patients at Risk for Chronic Renal Allograft Failure
Bibliographic record
Abstract
BACKGROUND: Chronic allograft nephropathy is the most frequent cause of long-term kidney allograft loss. Studies are desperately needed to improve long-term survival. Tacrolimus has been associated with less rejection and better kidney function compared with cyclosporine in clinical trials. This study tested the hypothesis that conversion from cyclosporine to tacrolimus might improve long-term outcomes in patients with chronic allograft damage. METHODS: In this multicenter Canadian clinical trial, cyclosporine-treated patients with biopsy-proven chronic allograft nephropathy and impaired renal function were randomly assigned (2:1) to convert to tacrolimus or continue on cyclosporine therapy. A total of 106 (70 tacrolimus and 36 cyclosporine treated) patients were followed-up for up to 5 years. The primary outcome was graft survival. RESULTS: In an intention to treat analysis, subsequent graft (73% vs. 81%, P=0.2835, log-rank test) and patient survival (91% vs. 92%, P=0.8668, log-rank test) were not different between the tacrolimus and cyclosporine groups, respectively. Changes in Chronic Allograft Damage Index scores on protocol biopsies from baseline to 3 years were not different (+0.4+/-1.8 vs. +1.3+/-3.2, P=0.5910, cyclosporine vs. tacrolimus, respectively). There were no significant differences in biopsy-proven acute rejection (6 [8.6%] vs. 2 [5.6%], tacrolimus vs. cyclosporine, respectively, P=0.5906). CONCLUSIONS: In this study, patients with chronic allograft damage converted from cyclosporine to tacrolimus demonstrated no apparent benefit.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".