Sex Differences in Cardiac Medication Use Post-Catheterization in Patients Undergoing Coronary Angiography for Stable Angina with Nonobstructive Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: Treatment of patients with stable angina and nonobstructive coronary artery disease (CAD) has not been well characterized. We comparatively evaluated medication use in males and females with stable angina with no CAD, nonobstructive CAD, and obstructive CAD. METHODS: We studied all patients ≥20 years old with stable angina undergoing coronary angiography in British Columbia (BC), Canada, from January 2008 to March 2010 (n = 7,535). No CAD, nonobstructive CAD, and obstructive CAD were defined as 0%, 1%-49%, and ≥50% luminal narrowing in any epicardial coronary artery, respectively. Medication use, 3 months before and 3 months following angiography, was obtained through BC PharmaNet for angiotensin-converting enzyme inhibitors (ACE-I), angiotensin receptor blockers (ARBs), calcium channel blockers (CCBs), beta-blockers, statins, antiplatelet agents, and prescriptions for all three ACE-I/ARBs, beta-blockers, and statins (combination therapy). RESULTS: Following angiography, patients with no and nonobstructive CAD had significantly lower rates of prescription use of all medications, including combination therapy, than patients with obstructive CAD (p < 0.001). Use of ACE-I/ARBs, beta-blockers, statins, and combination therapy did not differ by sex, but females had higher use of CCB in all CAD groups, and clopidogrel in nonobstructive and obstructive CAD groups, compared to males. CONCLUSIONS: In patients with stable angina, medication use following angiography is low in nonobstructive CAD with only 58.9% prescribed a statin and 19.4% on combination therapy at 3 months. There are no important sex differences in medication use in any CAD category post-angiography. Future studies should explore methods of improving quality of care in patients with nonobstructive CAD.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".