Efficacy and Tolerability of Rosuvastatin and Atorvastatin when Force-Titrated in Patients with Primary Hypercholesterolemia
Bibliographic record
Abstract
BACKGROUND: Patients at high risk of cardiovascular disease frequently fail to reach recommended low-density lipoprotein cholesterol (LDL-C) goals, partly because statin doses are not titrated to optimal effect. The ECLIPSE study was designed to compare the efficacy and safety of force-titrated treatment with rosuvastatin (10-40 mg) with that of atorvastatin (10-80 mg) in high-risk patients with hypercholesterolemia. METHODS: In this 24-week, open-label, randomized, multinational, parallel-group study, 1,036 patients were randomized to rosuvastatin (n = 522) or atorvastatin (n = 514). RESULTS: At all time points, a significantly greater percentage of patients on rosuvastatin treatment achieved the NCEP ATP III LDL-C goal of <100 mg/dl (2.5 mmol/l), the 2003 European LDL-C target of <2.5 or 3.0 mmol/l (100 or 115 mg/dl) and the LDL-C goal of <70 mg/dl (1.8 mmol/l), a goal suggested for very high-risk patients (p < 0.001 for all). Rosuvastatin also achieved significantly greater improvements in components of the atherogenic lipid profile versus atorvastatin. Both treatments were well tolerated. CONCLUSION: Rosuvastatin titrated across its recommended dose range provides a more favorable effect on lipoprotein variables than atorvastatin, enabling more high-risk patients to achieve recommended LDL-C goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".