Sex-related differences in demographics, diagnosis and management of patients with chronic coronary syndromes
Bibliographic record
Abstract
AIMS: The impact of sex-related factors on current clinical management and outcomes of chronic coronary syndromes (CCS) are unclear. METHODS: All patients belonging to the prospective, nationwide START registry were included. Their baseline characteristics, diagnostic workup, revascularization strategy, pharmacological treatment and 1-year clinical outcomes were compared with respect to sex overall and in age tertiles. RESULTS: A total of 5070 consecutive patients were included. Most patients were males (80.1%). As expected, the prevalence of females increased with age. Distribution of risk factors and history of cardiovascular disease were different depending on sex, as well as diagnostic workup, with lower use of exercise stress testing in women (25.1% vs. 36.7%, P < 0.0001). The use of coronary angiography was similar in the two groups. Women had lower rates of multivessel coronary artery disease (CAD) (33.0% vs. 40.6% P < 0.0001) and higher rates of nonobstructive CAD (18.3% vs. 11.3%, P < 0.0001). Rates of myocardial revascularization were similar, but women were more likely to receive percutaneous coronary intervention than men (84.3% vs. 77.8%, P < 0.0001) and less likely to receive surgical/hybrid revascularization (10.0% vs. 15.1%, P < 0.0001). At 12-month follow-up, no differences were observed for the combined endpoint of all-cause mortality, re-hospitalization for myocardial infarction, heart failure, stroke or myocardial revascularization between males and females; however, a significantly worse perceived quality of life was observed in women. CONCLUSIONS: In a large nationwide cohort of patients with CCS, clinical outcomes were not different depending on sex. However, several differences in the diagnostic work-up, treatment strategies and quality of life were found between sexes.
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 | 0.000 | 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) | 0.003 | 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".