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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".