Superior Outcomes in Renal Transplantation after Early Cyclosporine Withdrawal and Sirolimus Maintenance Therapy, Regardless of Baseline Renal Function
Bibliographic record
Abstract
BACKGROUND: It has become increasingly important to refine therapeutic strategies according to individual patient characteristics. We evaluated the long-term impact of renal function at the time of withdrawing cyclosporine (CsA) in renal allograft recipients receiving sirolimus (SRL), CsA, and steroids (ST). METHODS: At 3 months+/-2 weeks, 430 of 525 patients were eligible to be randomized to remain on triple-therapy (SRL-CsA-ST, n=215) or to have CsA withdrawn (SRL-ST, n=215). Patients were divided into quartiles according to their baseline (last value before randomization) calculated GFR: 45 to 56 ml/min (quartile 2, n=105), >56 to 67 ml/min (quartile 3, n=112), and >67 ml/min (quartile 4, n=107). All data were included (ITT analysis). RESULTS: At 4 years, calculated GFR for SRL-CsA-ST vs. SRL-ST was 22.1 vs. 37.7 ml/min (P=0.017), 38.6 vs. 56.6 ml/min (P<0.001), 50.7 vs. 66.8 ml/min (P=0.006), and 62.7 vs. 71.4 ml/min (P=0.436), for quartiles 1 to 4, respectively. Death-censored graft loss ranged from 21.2% vs. 7.7% (SRL-CsA-ST vs. SRL-ST, P=0.092) in quartile 1 to 5.5% vs. 1.9% (P=0.618) in quartile 4. The incidence of death and biopsy-confirmed acute rejection also decreased with increasing baseline GFR, but was not significantly different between treatments. Overall, more patients remained on therapy in the SRL-ST group (46.3% vs. 57.9%, P=0.020). CONCLUSIONS: Early and complete withdrawal of CsA from a combination of SRL, CsA, and steroids was preferable to continuing on this regimen, regardless of baseline renal function. The benefit was most marked in patients with a baseline calculated GFR
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".