Tacrolimus Formulation, Exposure Variability, and Outcomes in Kidney Transplant Recipients
Bibliographic record
Abstract
Introduction Few studies have compared within-patient variability measures of tacrolimus trough levels by formulation and assessed within-patient variability on outcomes of kidney transplant recipients. Research Questions (1) To compare within-patient variability of trough levels when converting from twice-daily to once-daily tacrolimus using standard deviation, coefficient of variation, and intrapatient variability percent. (2) To use the 3 measures of variability to examine the relationship between tacrolimus once-daily within-patient variability and total graft failure (i.e., return to chronic dialysis, pre-emptive retransplant, death with graft function). Design In this observational cohort study, within-patient variability of trough levels pre- and post-conversion from twice-daily to once-daily tacrolimus were compared using Wilcoxon matched-pairs signed-rank test. Graft outcomes were analyzed using Kaplan-Meier curves and multivariable Cox proportional hazards models. Results In 463 patients, within-patient variability differences pre- and post-conversion of median standard deviation, coefficient of variation, and intrapatient variability percent were −0.16 ( P = 0.09), −0.01 ( P = 0.52), and −1.41 ( P = 0.32), respectively. Post-conversion, every 1 unit increase in within-patient variability standard deviation and intrapatient variability percent and every 0.1 unit increase in the coefficient of variation was associated with an increased hazard ratio [1.19 ( P = 0.004), 1.02 ( P = 0.030), 1.13 ( P = 0.001), respectively] of total graft failure. Post-conversion, within-patient variability above cohort medians using standard deviation and coefficient of variation had a significantly higher risk of total graft failure. Discussion Under a program-wide conversion, no significant difference was observed in within-patient variability post-conversion from twice-daily to once-daily tacrolimus using the three measures of variability. High within-patient variability was associated with adverse transplant outcomes post-conversion.
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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".