Clinical benefits of neoral C2 monitoring in the long-term management of renal transplant recipients1
Bibliographic record
Abstract
BACKGROUND: Cyclosporine monitoring using the 2-hr postdose sample, C2, has been shown to have advantages in monitoring de novo renal transplant recipients. The purpose of this study was to assess cyclosporine exposure, using C2, in stable renal transplant patients previously monitored by C0 to determine the effect of dose reduction on patients with C2 more than 10% above target and the course of those with C2 at and more than 10% below target, whose dose was not modified. METHODS: One hundred and seventy-five patients, three or more months after transplantation, had C2 assessed. The relationship of C2 to C0 and of both to renal function was analyzed by linear regression. Blood pressure, serum creatinine level, and lipids were followed for a mean of 15+/-2.6 months. RESULTS: Eighty-five patients had values more than 10% above target, 42 were within 10% of target, and 48 were more than 10% below target. Cyclosporine dose was reduced in all patients above target. In this group, serum creatinine level was stable overall, but fell significantly in 46 (54%) of 85 from 153+/-55 to 132+/-49 microM. Blood pressure also fell in that group from 135/82 to 131/77. Serum creatinine level was stable in the remaining two groups of patients. CONCLUSIONS: These data suggest that dose reduction in many overexposed patients leads to improvements in renal function and blood pressure. Further study is required to confirm the long-term benefits of this strategy.
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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 | 0.003 |
| 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 | 0.000 | 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".