Limited sampling strategy for cyclosporine (Neoral<sup>®</sup>) area under the curve monitoring in pediatric kidney transplant recipients
Bibliographic record
Abstract
Cyclosporine (CSA; Neoral) is one of the most common immunosuppressants used in pediatric renal transplantation. Research in adult renal transplant recipients has shown that 2-h post-dose concentration (C2) monitoring and limited sampling strategies (LSSs) are better at predicting drug exposure and outcome than trough concentrations (C0). While C0 monitoring is the usual practice in pediatric renal transplant patients, area under the curve (AUC) monitoring has been shown to be superior in terms of predictive ability and outcomes. However, AUC monitoring is impractical and inconvenient in a clinic setting because it involves many blood samples. An LSS provides a reliable alternative. The purpose of this study was to prospectively define an LSS (AUC(0-12)) for CSA monitoring and to test its predictive performance. As well, an LSS (AUC(0-4)) for CSA was developed and its predictive performance tested. Blood samples for CSA concentrations were collected in 29 stable pediatric renal transplant patients prior to (t = 0) and at 0.5, 1, 2, 4, 6, and 8 h following a steady-state morning CSA dose. AUC was calculated by the trapezoidal method; LSSs for AUC(0-12) and AUC(0-4) were determined using multiple regression analysis in 14 patients; and the LSSs' predictive performance was tested in 15 additional patients. Both LSSs require two blood samples. For the LSS (AUC(0-12)), blood samples are required immediately before the dose and 2 h post-dose: AUC(0-12) = 12.45 C0 + 2.17 C2 + 723.16 (r2 = 0.909). For the LSS (AUC(0-4)), blood samples are required at one and 2 h post-dose, AUC(0-4) = 1.17 C1 + 1.85 C2 - 41.00 (r2 = 0.971). The LSSs demonstrated low bias and high precision for both AUC(0-12) and AUC(0-4). Our two-concentration LSSs are accurate and precise predictors that are more clinically useful for our patient population than other LSSs that have been developed for pediatric renal transplant patients. Our study template provides a guide for other centers to develop accurate and precise LSSs specific to their own patient population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".