Limited sampling strategies for sirolimus after pediatric renal transplantation
Bibliographic record
Abstract
SRL has been increasingly used in renal transplantation, but limited sampling approaches for estimation of AUC remain elusive. A post-hoc analysis of 94 PK profiles in 75 patients from four previous studies was performed to generate limited sampling approaches for approximation of AUC based on two to four time points for both BID and OD SRL dosing. AUC was calculated using the trapezoid rule. Stepwise linear regression was performed to generate an abbreviated AUC from the limited sampling approaches. For BID dosing, complete AUC had a strong correlation with the trough levels (r(2) = 0.882, p < 0.0001) and with C2 level (r(2) = 0.9025, p < 0.0001). A three-point and a four-point limited sampling approach showed improved agreement with complete AUC compared with single-point sampling. A convenient and accurate (r(2) = 0.992) four-point limited sampling approach reads: AUC = 10;(1.085 + 0.117 x log C0 + 0.164 x log C1-0.131 x log C2 + 0.823 x log C4). Similarly, complete AUC had a statistically significant correlation with the trough levels (r(2) = 0.549, p < 0.0001) and with C2 level (r(2) = 0.716, p < 0.0001) for OD dosing. The estimation of AUC for OD dosing was improved over single-point sampling (r(2) = 0.951) using the formula: AUC = 10;(1.100 + 0.115 x log C0 + 0.803 x log C4). This study represented the first limited sampling approach for SRL. Further studies are required to determine the optimal SRL target AUC.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".