Abbreviated mycophenolic acid AUC from CO, C1, C2, and C4 is preferable in children after renal transplantation on mycophenolate mofetil and tacrolimus therapy
Bibliographic record
Abstract
Abstract In order to allow a similar algorithm to be used for both adults and children on tacrolimus-based and mycophenolate mofetil [MMF, a pro-drug for mycophenolic acid (MPA)]-based immunosuppression, a limited sampling technique from the trough level (C0) and the levels 30 min (C0.5) and 2 h (C2) after intake was to be developed from MPA area under the time-concentration curves (AUC). We retrospectively analyzed 49 full ten-point pharmacokinetic (PK) profiles from 29 pediatric patients on MMF and tacrolimus. We used stepwise multiple regression analysis to calculate limited sampling approaches. Agreement with the AUC was tested by means of Bland and Altman analysis. The correlation between AUC and pre-dose trough concentration was r2=0.5188 (P < 0.0001) and between AUC and post-dose trough concentration r2=0.6924 (P < 0.0001). The next best correlations were with 2 h (C2, r2=0.6711, P < 0.0001), 4 h (C4, r2=0.6411, P < 0.0001), 1.5 h (C1.5, r2=0.6344, P < 0.0001), and 6 h (C6, r2=0.6219, P < 0.0001). Three-point estimates at C0, C0.5, and C2 resulted in an acceptable correlation between predicted AUC and AUC from the full profile when we used the formula AUC = 10.01391 + 3.94791xC0 + 3.24253 xC0.5 + 1.0108xC2, Pearson's r= 0.8996, 95% confidence interval 0.8277–0.9424. However, even better results could be obtained when we used AUC = 8.217 + 3.163xC0 + 0.994 xC1 + 1.334xC2 + 4.183 xC4, Pearson's r= 0.9456, 95% confidence interval 0.9051–0.9691. Bland and Altman analysis revealed good agreement between AUC predicted from C0, C0.5, and C2 and AUC from the full profile, but was inferior to the four-point approach. Also, the previously reported formula derived for adults was not usable in these patients. A special formula must be used for children. The AUC of MPA can be predicted by limited sampling including C0, C0.5, and C2, while an approach using C0, C1, C2, and C4 is preferable.
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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".