How to reach LDL targets quickly in patients with diabetes or metabolic syndrome.
Bibliographic record
Abstract
PURPOSE: To investigate whether using an algorithm to select the starting dose of a statin according to baseline and target LDL-cholesterol (LDL-C) values would facilitate achieving lipid targets in patients with diabetes or the metabolic syndrome. METHODS: Two 12-week, prospective, open-label trials enrolled 2717 high-risk subjects, of whom 1024 had diabetes and 1251 had metabolic syndrome. Subjects with LDL-C between 100 and 220 mg/dL (2.6-5.7 mmol/L) were assigned a starting dose of atorvastatin (10, 20, 40, or 80 mg/d) based on LDL-C level and status of statin use at baseline (statin-free [SF] or statin-treated [ST]), with a single uptitration at 6 weeks, if required. RESULTS: Among patients with diabetes, 81% of SF subjects (82%, 84%, 82%, and 76% with 10, 20, 40, and 80 mg, respectively) and 60% of ST subjects (61%, 68%, and 47% with 20, 40, and 80 mg, respectively) achieved LDL-C target. Among patients with metabolic syndrome, 78% of SF subjects (81%, 84%, 82%, and 66% with 10, 20, 40, and 80 mg, respectively) and 57% of ST subjects (58%, 70%, and 47% with 20, 40, and 80 mg, respectively) achieved LDL-C target. Among ST subjects, we observed reductions in LDL-C with atorvastatin beyond those achieved with other statins used at baseline in patients with diabetes and patients with metabolic syndrome. Atorvastatin was well tolerated. CONCLUSIONS: The ACTFAST studies confirm that a targeted starting dose of atorvastatin allows most patients with type 2 diabetes or the metabolic syndrome to achieve their LDL-C target safely with the initial dose or just a single titration. This therapeutic strategy may help overcome the treatment gap still observed in the treatment of lipids in diabetes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".