Basiliximab lowers the cyclosporine therapeutic threshold in the early post‐kidney transplant period
Bibliographic record
Abstract
Early adequate cyclosporine exposure has been shown to predict low acute rejection rate in kidney transplantation. The aim of this study is to determine the importance of exceeding the early cyclosporine therapeutic exposure threshold with basiliximab induction. A retrospective analysis of 166 first cadaveric and non-identical live donor transplant recipients treated with or without basiliximab induction, Neoral, mycophenolate mofetil and prednisone, was performed. Adequate exposure was defined as a 2-h post-Neoral dose cyclosporine level (C2) >1700 ng/mL at day 3. The primary outcome was acute rejection within the first 6 months. In the no basiliximab (control) group (n = 74), rejection occurred in 23% (17 of 74) of recipients and was strongly associated with low cyclosporine exposure on day 3. Acute rejection occurred in 38% (11 of 29) with C2 <1700 ng/mL compared with 13% (six of 45) with C2 >/=1700 ng/mL (p = 0.014). In the basiliximab group (n = 92), rejection occurred in only 11% (10 of 92) of recipients and did not correlate with cyclosporine exposure. Acute rejection occurred in 10% (four of 40) with C2 <1700 ng/mL compared with 12% (six of 52) with C2 >/=1700 ng/mL (p = 0.81). Therefore achieving cyclosporine therapeutic targets by day 3 may not be required when anti-IL2 receptor antibody induction is used.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".