A comparison of the extended-release and standard-release formulations of tacrolimus in<i>de novo</i>kidney transplant recipients: a 12-month outcome study
Bibliographic record
Abstract
BACKGROUND: Limited comparative data are available on the outcomes between extended-release and standard-release tacrolimus when used de novo in kidney transplant recipients (KTRs). METHODS: We identified KTRs transplanted at our institution during 2009-10 routinely prescribed extended-release tacrolimus and compared them with those transplanted during 2008-09 prescribed standard-release tacrolimus. Graft function (eGFR by MDRD-7 equation) at 12 months post-transplant (primary outcome); new-onset diabetes and other cardiovascular risk factors, BK viremia incidence, acute rejection, and graft survival to 12 months (secondary outcomes) were compared by intent-to-treat analysis. Time-to-steady-state concentration and number of dose adjustments required to attain steady state were recorded. RESULTS: There were no important demographic differences between the extended-release (N = 106) and standard-release (N = 95) cohorts. The estimated glomerular filtration rate (eGFR) at 12 months was similar (58.8 ± 17 versus 59.2 ± 18 mL/min/1.73 m(2), P = 0.307). There was no difference in new-onset diabetes (17 versus 20%, P = 0.581), BK viremia (10 versus 7%, P = 0.450), acute rejection (7 versus 16%, P = 0.067) or graft survival (97 versus 95%, P = 0.301). Time-to-steady state was similar (9.2 ± 1.1 versus 8.1 ± 4.7 days, P = 0.490) although extended-release patients required fewer adjustments to attain steady state (1.2 ± 1.7 [0-8] versus 1.7 ± 1.5 [0-7], P = 0.030) but a similar dose (7.2 ± 2.4 [2-17] versus 7 ± 2.7 [2-16] mg/day, P = 0.697). CONCLUSION: De novo KTRs prescribed extended-release or standard-release tacrolimus demonstrate similar 12-month 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".