Attainment of Canadian and European guidelines’ lipid targets with atorvastatin plus ezetimibe vs. doubling the dose of atorvastatin
Bibliographic record
Abstract
BACKGROUND: Canadian and European treatment guidelines identify low-density lipoprotein cholesterol (LDL-C) as a primary treatment target for hypercholesterolaemia. OBJECTIVES: This post hoc analysis compared ezetimibe 10 mg (ezetimibe) added to atorvastatin vs. doubling the atorvastatin dose on achievement of the 2009 Canadian Cardiovascular Society (CCS) and the 2007 Joint European Prevention Guidelines primary and optional secondary lipid targets and high-sensitivity C-reactive protein (hs-CRP) levels. METHODS: After stabilisation on atorvastatin, hypercholesterolaemic patients at moderately high risk (MHR) for coronary heart disease (CHD) not at LDL-C < 2.6 mmol/l were randomised to atorvastatin 20 mg vs. doubling their atorvastatin dose to 40 mg; and patients at high risk (HR) for CHD not at LDL-C < 1.8 mmol/l were randomised to atorvastatin 40 mg plus ezetimibe vs. doubling their atorvastatin dose to 80 mg for 6 weeks. RESULTS: When treated with atorvastatin plus ezetimibe, MHR and HR patients had greater attainment of LDL-C, most lipids and lipoproteins and/or hs-CRP targets compared with doubling their atorvastatin dose. More MHR and HR patients achieved dual targets of LDL-C and: Apolipoprotein (Apo) B, total cholesterol (total-C), total-C/high-density lipoprotein cholesterol (HDL-C), non-HDL-C, triglycerides, Apo B/Apo A-I or hs-CRP with ezetimibe + atorvastatin treatment compared with doubling their atorvastatin dose. CONCLUSIONS: These results demonstrated greater achievement of single/dual treatment targets as set by Canadian and European treatment guidelines with ezetimibe added to atorvastatin 20 mg or 40 mg compared with doubling the atorvastatin dose to 40 mg or 80 mg in MHR and HR patients, respectively.
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 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 | 0.014 | 0.014 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".