Comparison of the effects of standard vs low-dose prolonged-release tacrolimus with or without ACEi/ARB on the histology and function of renal allografts
Bibliographic record
Abstract
Targeting the renin-angiotensin system and optimizing tacrolimus exposure are both postulated to improve outcomes in renal transplant recipients (RTRs) by preventing interstitial fibrosis/tubular atrophy (IF/TA). In this multicenter, prospective, open-label controlled trial, adult de novo RTRs were randomized in a 2 × 2 design to low- vs standard-dose (LOW vs STD) prolonged-release tacrolimus and to angiotensin-converting enzyme inhibitors/angiotensin II receptor 1 blockers (ACEi/ARBs) vs other antihypertensive therapy (OAHT). There were 2 coprimary endpoints: the prevalence of IF/TA at month 6 and at month 24. IF/TA prevalence was similar for LOW vs STD tacrolimus at month 6 (36.8% vs 39.5%; P = .80) and ACEi/ARBs vs OAHT at month 24 (54.8% vs 58.2%; P = .33). IF/TA progression decreased significantly with LOW vs STD tacrolimus at month 24 (mean [SD] change, +0.42 [1.477] vs +1.10 [1.577]; P = .0039). Across the 4 treatment groups, LOW + ACEi/ARB patients exhibited the lowest mean IF/TA change and, compared with LOW + OAHT patients, experienced significantly delayed time to first T cell-mediated rejection. Renal function was stable from month 1 to month 24 in all treatment groups. No unexpected safety findings were detected. Coupled with LOW tacrolimus dosing, ACEi/ARBs appear to reduce IF/TA progression and delay rejection relative to reduced tacrolimus exposure without renin-angiotensin system blockade. ClinicalTrials.gov identifier: NCT00933231.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".