Limited Sampling Strategies for Monitoring Tacrolimus in Pediatric Liver Transplant Recipients
Bibliographic record
Abstract
OBJECTIVE: To develop and validate limited sampling strategies (LSSs) for tacrolimus in pediatric liver transplant recipients. METHODS: Thirty-six 12-hour pharmacokinetic profiles from 28 pediatric liver transplant recipients (0.4-18.5 years) were collected. Tacrolimus concentrations were measured by immunoassay and area under the curve (AUC0-12) was determined by trapezoidal rule. LSSs consisting of 1, 2, 3, or 4 concentration-time points were developed using multiple regression analysis. Eight promising models (2 per category) were selected based on the following criteria: r2 ≥ 0.90, inclusion of trough concentration (C0), and time points within 4 hours postdose. The predictive performance of these LSSs was evaluated in an independent set of data by measuring the mean prediction error and the root mean squared prediction error. RESULTS: Five models including 2-4 time points predicted AUC0-12 with a ±15% error limit. Bias (mean prediction error) and precision (root mean squared prediction error) of LSS involving C0, C1, and C4 (AUCpredicted = 9.30 + 3.69 × C0 + 2.19 × C1 + 4.69 × C4) were -4.98% and 8.29%, respectively. Among single time point LSSs, the model using C0 had a poor correlation with AUC0-12 (r2 = 0.53), whereas the one with C4 had the highest correlation with tacrolimus exposure (r2 = 0.84). CONCLUSIONS: Trough concentration is a poor predictor of tacrolimus AUC0-12 in pediatric liver transplant recipients. However, LSSs using 2-4 concentration-time points obtained within 4 hours postdose provide a reliable and convenient method to predict tacrolimus exposure in this population. The proposed LSSs represent an important step that will allow the undertaking of prospective trials aiming to better define tacrolimus target AUC in pediatric liver transplant recipients and to determine whether AUC-guided monitoring is superior to C0-based monitoring in terms of efficacy and safety.
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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 | 0.000 |
| 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) | 0.000 | 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".