Effects of Fluvastatin on Cardiac Events in Renal Transplant Patients: ALERT (Assessment of Lescol(R) in Renal Transplantation) Study Design and Baseline Data
Bibliographic record
Abstract
BACKGROUND: Recent clinical trials of primary and secondary prevention of cardiovascular disease have demonstrated that lowering plasma cholesterol with 3-hydroxy-3-methylglutaryl coenzyme A (HMG CoA) reductase inhibitors ('statins') reduces morbidity and mortality from coronary heart disease in diverse patient populations. STUDY AIMS: The aim of the present ALERT (Assessment of Lescol in Renal Transplantation) study is to determine whether renal transplant recipients would also benefit from statin therapy. ALERT is a multicentre, randomized, double-blind, placebo-controlled trial to assess the effect of fluvastatin in renal transplant recipients with mild-to-moderate hypercholesterolaemia. The primary objective is to investigate the effects of fluvastatin on major adverse cardiac events (MACE). In addition, the effects on cardiovascular and all-cause mortality, as well as renal function, will be addressed. STUDY POPULATION: The study population contains patients with functioning renal allografts of more than 6 months' duration, recruited from 75 centres in Northern Europe and Canada. Patients of both sexes, aged 30-75 years, with a total cholesterol level of 4.0-9.0 mmol/l (155-348 mg/dl) were included, except for those with a history of myocardial infarction, where the upper limit for inclusion was 7.0 mmol/l (270 mg/dl). STUDY DESIGN: A total of 2100 patients were recruited by the end of October 1997 and will be followed for up to 6 years. This report presents the design features of the study (recruitment, follow-up, sample size, data analysis and study organization), along with baseline results. ALERT is the first large-scale prospective, randomized, double-blind study to address the prevention of cardiovascular mortality in renal transplant patients receiving an HMGCoA reductase inhibitor.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".