Risk Factors for Reaching Renal Endpoints in the Assessment of Lescol in Renal Transplantation (ALERT) Trial
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to identity risk factors for long-term renal transplant function and development of chronic allograft nephropathy (CAN) in renal transplant recipients included in the Assessment of Lescol in Renal Transplantation (ALERT) trial. METHODS: The ALERT trial was a randomized, double-blind, placebo-controlled study of the effect of fluvastatin, 40 and 80 mg/day, in renal transplant recipients who were randomized to receive fluvastatin (Lescol) (n = 1,050) or placebo (n = 1,052) over 5 to 6 years of follow-up. Renal endpoints including graft loss or doubling of serum creatinine or death were analyzed by univariate and multivariate regression analysis in the placebo group. RESULTS: There were 137 graft losses (13.5%) in the placebo group, mainly caused by CAN (82%). Univariate risk factors for graft loss or doubling of serum creatinine were as follows: serum creatinine, proteinuria, hypertension, pulse pressure, time since transplantation, donor age, human leukocyte antigen-DR mismatches, treatment for rejection, low high-density lipoprotein cholesterol, and smoking. Multivariate analysis revealed independent risk factors for graft loss as follows: serum creatinine (relative risk [RR], 3.12 per 100-microM increase), proteinuria (RR, 1.64 per 1-g/24 hr increase), and pulse pressure (RR, 1.12 per 10 mm Hg), whereas age was a protective factor. With patient death in the composite endpoint, diabetes mellitus, smoking, age, and number of transplantations were also risk factors. CONCLUSIONS: Independent risk factors for graft loss or doubling of serum creatinine or patient death are mainly related to renal transplant function, proteinuria, and blood pressure, which emphasizes the importance of renoprotective treatment regimens in this population.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 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".