Early cyclosporine withdrawal from a sirolimus-based regimen results in better renal allograft survival and renal function at 48 months after transplantation
Bibliographic record
Abstract
We report the 48-month results of a trial testing whether withdrawal of cyclosporine (CsA) from a sirolimus (SRL)-CsA-steroid (ST) regimen would impact renal allograft survival. Eligible patients receiving SRL-CsA-ST from transplantation were randomly assigned at 3 months to remain on triple therapy (SRL-CsA-ST, n = 215) or to have CsA withdrawn and SRL trough concentrations increased (SRL-ST, n = 215). SRL-ST therapy resulted in significantly better graft survival, either when including death with a functioning graft as an event (84.2% vs. 91.5%, P = 0.024) or when censoring it (90.6% vs. 96.1%, P = 0.026). Calculated glomerular filtration rate (43.8 vs. 58.3 ml/min, P < 0.001) and mean arterial blood pressure (101.3 vs. 97.1 mmHg, P = 0.047) were also improved with SRL-ST. Differences in the incidences of biopsy-proven acute rejection after randomization (6.5% vs. 10.2%, SRL-CsA-ST versus SRL-ST, respectively) and mortality (7.9% vs. 4.7%) were not significant. SRL-CsA-ST-treated patients had significantly higher incidences of adverse events generally associated with CsA, whereas those in the SRL-ST group experienced greater frequencies of events commonly related to higher trough levels of SRL. In conclusion, early withdrawal of CsA from a SRL-CsA-ST regimen rapidly improves renal function and ultimately results in better graft survival.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".