Universal approach to pharmacokinetic monitoring of immunosuppressive agents in children
Bibliographic record
Abstract
Current data indicate that pharmacokinetic (PK) monitoring of cyclosporin microemulsion (CsA) should be performed using the 2-h concentration (C2), that tacrolimus (Tac) is commonly monitored using the trough level, and mycophenolate mofetil (MMF) should be monitored using the 1-h (C1), 2-h (C2) and 6-h (C6) concentrations. The three differing time-point requirements are cumbersome, and we aimed to develop universal guidelines for all three drugs using a large number of full PK profiles in children. One-hundred and twenty two stable pediatric patients, receiving either CsA (165 PK profiles, 69 patients, 24 with concomitant MMF) or Tac (122 PK profiles, 53 patients, 18 with MMF) were analyzed retrospectively. Pearson r for the CsA C2 was 0.90 [95% confidence interval(CI): 0.86-0.92], for Tac C2 r was 0.86 (95% CI: 0.80-0.90), and for MPA C2 r was 0.77 (95% CI: 0.68-0.83), respectively. For MPA, at least three time-points are required to accurately estimate the area under the concentration-time curve (AUC), and C1, C2 and C6 serve as best markers. Excellent AUC estimations could be obtained from a limited sampling strategy from C1, C2 and C6 or C0, C1, C2 and C4 with clinically acceptable errors for all three drugs. The AUC can be estimated with great precision by using an identical approach for all three drugs. Target AUCs for a given time-point after transplantation remain to be established.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".