Preventive Lipid-Lowering Therapy and Interactions With Health Care in Patients Who Develop Premature Coronary Artery Disease
Bibliographic record
Abstract
Background: Rates of premature coronary artery disease (CAD) are stagnant, and the prevalence of cardiovascular risk factors in young and middle-aged adults is increasing. Lipid-lowering therapy (LLT) is effective in preventing CAD but is underutilized in younger patients. The reasons for and consequences of this underutilization are not fully understood. Objectives: The purpose of the study was to assess prepresentation health care encounters, eligibility for, usage patterns, and predictors of initiation of LLT and its relationships with the severity of clinical presentation of CAD. Methods: Using administrative databases and a clinical registry, we analyzed health care encounters, cardiovascular risk, and medication dispensations in females <55 and males <50 years old who presented with angiographically confirmed premature CAD. Results: Among 11,445 patients (27.6% females, age 46.14 ± 5.05 years) in the administrative database, in the 3 years before presentation, 93.3% were eligible for lipid screening and 92.2% had health care visits, but only 14.8% received LLT dispensations, and 5.9% displayed good adherence. In multivariable analysis, females (OR: 0.75; 95% CI: 0.65-0.86), rural residents (OR: 0.75; 95% CI: 0.62-0.91), and smokers (OR: 0.65; 95% CI: 0.57-0.74) were less likely to receive LLT. High-intensity LLT vs no LLT was associated with lower odds of presenting with acute coronary syndrome (OR: 0.25; 95% CI: 0.19-0.38). Among 470 clinical registry participants (27.4% females, mean age 45.72 ± 5.07 years), 70.2% had lipids assessed, 55.7% were eligible for LLT based on the estimated cardiovascular risk, 18.9% received treatment recommendations, and 12.1% received dispensations of LLT before presentation. Conclusions: Prior to presenting with premature CAD, most patients had medical encounters, but few received LLT, demonstrating a substantial gap in prevention.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".