P629Undertreatment of female patients in lipid-lowering for secondary prevention in Europe, Canada, South Africa, Middle East and China: results of the Dyslipidemia International Study (DYSIS)
Bibliographic record
Abstract
Background: Recent guidelines of EAS/ESC as well as AHA/ACC recommend LDL-C <70 mg/dl in very high risk patients. Despite chronic statin treatment, only a minority of patients achieve this target. Methods: Between 2008 and 2012, consecutive statin-treated outpatients were enrolled in 26 countries including Europe, Canada, South-Africa, Middle East and China, (DYSIS = Dyslipidemia International Study) to assess LDL-C goal attainment for secondary prevention. Data were collected under real life conditions in the outpatient setting. We examined the impact of female gender on LDL-target-achievement. Results: A total of 46,310 patients of DYSIS were at very high risk, of whom 18,653 (40.3%) were females. Female patients were older, more often had risk factors such as hypertension and diabetes, but less often suffered from already manifest ischemic heart disease as compared to the male population. Females more often were treated with less potent statins as well as with lower doses of statins independent on the statin used. Even after correcting for differences in baseline characteristics female gender was an independent predictor of not achieving LDL-C-targets in clinical practice (OR 0.68; 95% CI 0.47–0.97).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".