A review of the efficacy of rosuvastatin in patients with type 2 diabetes
Bibliographic record
Abstract
It has been estimated that 92% of individuals with type 2 diabetes, without cardiovascular disease (CVD), have a dyslipidaemic profile. Several guidelines on cardiovascular risk now recommend that patients with diabetes should be considered at high risk of CVD and should thus receive lipid-lowering therapy to reduce low-density lipoprotein cholesterol (LDL-C) to below 2.5 mmol/L. Since their introduction in 1987, statins have revolutionized the management of CVD. The most recent statin to be introduced, rosuvastatin, has been shown to be the most effective at lowering LDL-C, as well as consistently raising HDL-C across the 10-40 mg dose range. This has been confirmed by many studies, including the Measuring Effective Reductions in Cholesterol Using Rosuvastatin Therapy (MERCURY I) study in which rosuvastatin 10 mg was shown to be more effective than commonly used doses of other statins, both for LDL-C reduction and achieving treatment target goals. The effectiveness of rosuvastatin has also been studied in type 2 diabetes patients in three studies: the URANUS (Use of Rosuvastatin vs. Atorvastatin iN type 2 diabetes mellitUS), ANDROMEDA (A raNdomized, Double-blind study to compare Rosuvastatin [10 & 20 mg] and atOrvastatin [10 & 20 Mg] in patiEnts with type II DiAbetes) and CORALL (COmpare Rosuvastatin [10-40 mg] with Atorvastatin [20-80 mg] on apo B/apo A-1 ratio in patients with type 2 diabetes meLLitus and dyslipidaemia) studies. URANUS and ANDROMEDA showed rosuvastatin to be more effective than atorvastatin at reducing LDL-C and achieving treatment target goals. CORALL demonstrated rosuvastatin 10, 20 and 40 mg to be more effective at lowering LDL-C than 20, 40 and 80 mg of atorvastatin, respectively. Ongoing studies will evaluate whether these properties of rosuvastatin translate into beneficial effects on atherosclerosis and significant reductions in cardiovascular events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".