Sex Bias and Underutilization of Lipid-Lowering Therapy in Patients With Coronary Artery Disease at Academic Medical Centers in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: The efficacy of lipid-lowering therapy (LLT) has been well established for patients with preexisting coronary artery disease (CAD). However, limited information is available assessing the extent to which these medications are prescribed in academic medical centers. METHODS: The use of LLT for patients with CAD was prospectively evaluated in 825 men and women who were recruited from 16 academic medical centers in the United States and Canada to participate in the Prospective Evaluation of the Vascular Events of Norvasc Trial (PREVENT). The assessment of LLT use during the 3-year trial was evaluated in patients receiving amlodipine therapy and placebo; levels of low-density lipoprotein cholesterol (LDL-C) were used to assess the impact of LLT. RESULTS: Despite a baseline prevalence of LLT in 42% of men (38% in 1994), half of the patients had high levels of LDL-C (>3.36 mmol [>130 mg/dL]). During the subsequent 3 years, the prevalence of elevated LDL-C levels dropped in men (29%) but remained stagnant in women (48%). These changes were associated with increased LLT in men (55%) but not in women (35%) (P = .04). In 1994, the LDL-C target goal (<2.59 mmol/L [<100 mg/dL]) was attained in 17% of men and 6% of women (P = .006). At study completion in 1997, the LDL-C target goal was achieved in 31% of men and only 12% of women (P = .001). CONCLUSIONS: This study highlights the relatively low treatment rates of hyperlipidemia among patients with CAD overall and women in particular who were participating in a clinical trial at academic medical centers in the United States and Canada. Because LLT has been proven to reduce future cardiovascular events, these results suggest that more intensive efforts should be promoted in order to maximize CAD reduction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".