Limited Sampling Strategies for Estimating Intravenous and Oral Cyclosporine Area Under the Curve in Pediatric Hematopoietic Stem Cell Transplantation
Bibliographic record
Abstract
BACKGROUND: The optimal monitoring strategy for cyclosporine (CsA) in pediatric hematopoietic stem cell transplantation (HSCT) patients remains unclear. Although there is a growing interest in the use of the area under the concentration-time curve (AUC), measurement of AUC in clinical settings is often impractical. The objective of this study was to identify and validate limited sampling strategies (LSSs) for the prediction of CsA AUC after intravenous (IV) and oral (PO) administration in this population. METHODS: Sixty-eight pediatric patients who underwent HSCT and received CsA were investigated. Twelve-hour pharmacokinetic profiles (n = 138) performed per standard of care were collected. Weighted multiple linear regression was used to investigate all possible LSSs consisting of 4 or less concentration-time points. Their predictive performance was evaluated by leave one out cross validation and external validation by measuring the root mean squared relative error (RMSE%) and the 95th percentile of the absolute relative error (AE%). Values less than 20% were considered clinically acceptable. RESULTS: Nine LSSs (4 IV and 5 PO) convenient for clinical application proved to have clinically acceptable performance. Notably, LSS based on C0, C2, and C4 was found to be accurate for estimation of CsA exposure after both IV and PO administration with the 95th percentile of AE% of 19.7% and 17.5%, respectively. CONCLUSIONS: LSSs using 3 or 4 concentration-time points obtained within 4 hours postdose provide a convenient and reliable method to estimate CsA exposure in this population. These LSSs may facilitate future research aiming at better defining the relationship between AUC and clinical outcomes.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".