Relationship between LDL-C and non-HDL-C levels and clinical outcome in the GREek Atorvastatin and Coronary-heart-disease Evaluation (GREACE) study
Bibliographic record
Abstract
BACKGROUND: Although available guidelines suggest reducing low-density lipoprotein cholesterol (LDL-C) to below 100 mg/dL (2.6 mmol/L), the importance of target-oriented therapy remains controversial. To assess whether achieving guideline-based targets is of benefit, the relationship between clinical outcomes and lipid levels (baseline and on-study) was evaluated in the GREek Atorvastatin and Coronary-heart-disease Evaluation (GREACE) study. This study demonstrated significant reductions in morbidity and mortality associated with active dose titration of atorvastatin and structured management of dyslipidaemia. METHODS AND RESULTS: Intention-to-treat analysis (Cox proportional hazards model) was used to assess the relationship between lipid values and coronary events. Higher levels of LDL-C at baseline were associated with a greater risk of subsequent events among patients randomized to usual care. Reducing the LDL-C and the non-high density lipoprotein cholesterol (non-HDL-C) level to the National Cholesterol Educational Program (NCEP) Adult Treatment Panel (ATP) III goals required greater doses of atorvastatin for the higher baseline quartile of LDL-C. During the study there was a greater reduction in the risk of coronary heart disease (CHD) events in atorvastatin-treated patients who were in the highest quartile of LDL-C at baseline, after achieving the LDL-C treatment goal, in comparison to the usual care patients in the highest baseline LDL-C quartile. CONCLUSIONS: Achieving the NCEP ATP III LDL-C and non-HDL-C goals by titrating up the dose of atorvastatin was associated with a significant reduction in vascular events in patients with CHD. The greatest benefit was seen in those patients with the highest baseline LDL-C levels.
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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 | 0.018 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".