Randomized trial of tacrolimus + mycophenolate mofetil or azathioprine versus cyclosporine + mycophenolate mofetil after cadaveric kidney transplantation: results at three years
Bibliographic record
Abstract
METHODS: Two hundred twenty-three recipients of first cadaveric kidney allografts were randomized to receive tacrolimus (TAC) + mycophenolate mofetil (MMF), TAC + azathioprine (AZA), or cyclosporine (Neoral; CsA) + MMF. All regimens contained corticosteroids, and antibody induction was used only in patients who experienced delayed graft function (DGF). Patients were followed-up for 3 years. RESULTS: The results at 3 years corroborate and extend the findings of the 2-year results. Patients with DGF treated with TAC+MMF experienced an increase in 3-year allograft survival compared with patients receiving CsA+MMF (84.1% vs. 49.9%, P=0.02). Patients randomized to either treatment arm containing TAC exhibited numerically superior kidney function when compared with CsA. During the 3 years, new-onset insulin dependence occurred in 6, 3, and 11 patients in the TAC+MMF, CsA+MMF, and TAC+AZA treatment arms, respectively. Furthermore, patients randomized to TAC+MMF received significantly lower doses of MMF as compared with those who received CsA+MMF. CONCLUSION: All three immunosuppressive regimens provided excellent safety and efficacy. However, the best results overall were achieved with TAC+MMF. The combination may provide particular benefit to kidney allograft recipients with DGF. In patients who experienced DGF, graft survival was better at 3 years in those patients receiving TAC in combination with either MMF or AZA as compared with the patients receiving CsA with MMF.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".