Bibliographic record
Abstract
BACKGROUND AND PURPOSE: There has been recent controversy over failure of ezetimibe to reduce carotid intima-media thickness. Much of this is based on failure to understand important differences among ultrasound phenotypes of atherosclerosis. METHODS: We analyzed the effect of adding ezetimibe to the regimen of patients being followed in vascular prevention clinics where measurement of carotid plaque burden (total plaque area) is used to guide therapy. RESULTS: There were complete data in 231 patients with total plaque area for 2 years before and 2 years after initiation of ezetimibe. In the 2 years before and after initiation of ezetimibe, total cholesterol decreased significantly before (P<0.0001) and after initiation of ezetimibe (P<0.0001); low-density lipoprotein cholesterol declined significantly before (P<0.0001) and after (P=0.003) initiation of ezetimibe. Triglycerides declined significantly before ezetimibe (P<0.0001) but did not change after addition of ezetimibe (P=0.48). High-density lipoprotein cholesterol did not change significantly before (P=0.87) but declined significantly after ezetimibe (P=0.03). Despite the decline in low-density lipoprotein cholesterol before addition of ezetimibe, there was a significant mean increase in within-individual total plaque area in the 2 years before addition of ezetimibe by 6.89±39.57 mm(2) (SD); after addition of ezetimibe, despite the decline in high-density lipoprotein, plaque area decreased by -3.05±SD 38.18 mm(2) SD (P<0.01). CONCLUSIONS: Ezetimibe appears to regress carotid plaque burden. To assess effects of antiatherosclerotic therapies, it is important to measure plaque burden. These findings should be tested in a clinical trial.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".