What is the intrapatient variability of mycophenolic acid trough levels?
Bibliographic record
Abstract
TDM of MPA, the active compound of MMF, is rarely used despite its substantial intra- and interpatient variability. Little is known about the utility of long-term MPA TDM. Data are expressed as mean (one standard deviation). All available data from 27 renal transplant recipients (mean age at transplantation: 7.7 [5.0] yr) with an average follow-up of 9.3 (4.6) yr were analyzed. MPA levels were measured using the EMIT. GFR was measured using cystatin C and eGFR was calculated using the Filler formula. Intrapatient CV of the trough level was calculated as the ratio of the mean divided by one standard deviation. Mean cystatin C eGFR was 56.9 (24.4) mL/min/1.73 m(2) . There was a weak but significant correlation between the MPA trough level and the AUC (Spearman r = 0.6592, p < 0.0001). A total of 1964 MPA trough levels (73 [45]/patient) were measured, as compared to 3462 Tac trough levels (144 [71]/patient). The average MPA trough level was 3.01 (1.26) mg/L and the average trough Tac level was 7.3 (1.8) ng/mL. Intrapatient CV was statistically higher (p = 0.00093) for MPA at 0.68 (0.29) when compared to Tac with a CV of 0.46 (0.12). CV did not correlate with eGFR. Intrapatient MPA trough level CV is significantly higher than for Tac, while CV for both MPA and Tac was high. MPA trough level monitoring may be a feasible monitoring option to improve patient exposure and possibly 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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".