Adequate Early Cyclosporin Exposure is Critical to Prevent Renal Allograft Rejection: Patients Monitored by Absorption Profiling
Bibliographic record
Abstract
This study used receiver operating characteristic analysis to investigate the properties of area under the concentration-time curve during the first 4h after cyclosporin-microemulsion dosing (AUC0-4) and cyclosporin (CyA) levels immediately before and at 2 and 3h after dosing (C0, C2 and C3) to predict the risk of biopsy-proven acute rejection (AR) at 6 months. Ninety-eight kidney transplant recipients treated with CyA-microemulsion-based triple therapy immunosuppression were studied on post-transplant days 3, 5, and 7, and at increasing intervals thereafter. The most sensitive and specific predictor of AR was AUC0-4. Of the single time-point measurements, the measurement properties of C2 were closest to those of AUC0-4, and superior to those of C3. The relationship between C0 and subsequent AR was weak and did not reach statistical significance. On day 3, CyA AUC0-4 > or = 4,400 ng.h/mL and C2 > or = 1,700 ng/mL were each associated with a 92% negative predictive value for rejection in the first 6months. Pharmacokinetic measurements on or after day 5, and measurements on day 3 in patients with delayed graft function, were not predictive of AR. Adequate exposure within the first 3days post transplantation may be critically important in preventing subsequent rejection.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".