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 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".