Low‐Density Lipoprotein Cholesterol and High‐Sensitivity C‐Reactive Protein Lowering With Atorvastatin in Patients of South Asian Compared With European Origin: Insights From the Achieve Cholesterol Targets Fast With Atorvastatin Stratified Titration (ACTFAST) Study
Bibliographic record
Abstract
The aim of this study was to determine the effects of atorvastatin in patients of South Asian versus European origin who participated in the Achieve Cholesterol Targets Fast with Atorvastatin Stratified Titration (ACTFAST) study. ACTFAST was a 12-week prospective, open-label study in patients at high risk for atherosclerosis (European origin, n = 1978; South Asian origin, n = 64). Compared with patients of European origin, patients of South Asian origin were younger, were less likely to smoke, and had lower body mass index, systolic blood pressure, low-density lipoprotein cholesterol (LDL-C) and triglycerides. Because significant differences were observed in baseline characteristics between patient groups, case control propensity scores were used. In the unmatched analysis, South Asians had greater LDL-C response to atorvastatin than patients of European origin. However, after propensity matching, atorvastatin lowered LDL-C and high-sensitivity C-reactive protein (hs-CRP) to a similar degree in both groups, with no differences in safety profile. The authors observed no correlation between change in hs-CRP and LDL-C concentrations in either population. In conclusion, atorvastatin lowered both LDL-C and hs-CRP to a similar degree in patients of South Asian or European origin, suggesting usual starting doses of atorvastatin (with appropriate monitoring), rather than lower starting doses as has been advocated by some, may be used in patients of South Asian origin.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".