Risk factor profiles of young women with vasomotor non-obstructive versus obstructive coronary syndromes: Importance of non-traditional and sex-specific risk factors
Bibliographic record
Abstract
Background Heart disease is the leading cause of premature death for women in Canada. Ischemic heart disease (IHD) is categorized as myocardial infarction (MI) with no obstructive coronary artery disease (MINOCA), ischemia with no obstructive coronary arteries (INOCA), and atherosclerotic obstructive coronary artery disease (CAD) with MI (MI-CAD) or without MI (non-MI CAD). This study aims to study the prevalence of traditional and non-traditional IHD risk factors and their relationships with (M)INOCA compared to MI-CAD and non-MI CAD in young women. Methods This study investigated women who presented with premature (≤55 years old) vasomotor entities of (M)INOCA or obstructive CAD confirmed by coronary angiography, who are currently enrolled in either the Leslie Diamond Women’s Heart Health Clinic Registry (WHC) or the Study to Avoid cardioVascular Events in BC (SAVEBC). Univariable and multivariable regression models were applied to investigate associations of risk factors with odds of (M)INOCA, MI-CAD or non-MI CAD. Results A total of 254 women enrolled between 2015-2022 were analyzed: 77 INOCA and 37 MINOCA from the WHC and 66 with non-MI CAD and 74 MI-CAD from SAVEBC. Regression analyses demonstrated that migraines and preeclampsia/gestational hypertension were the most significant risk factors with higher likelihood to associate with premature (M)INOCA relative to obstructive CAD. Conversely, the presence of diabetes and a current or previous smoking history had the highest likelihood to associate with premature CAD. Conclusion There are significant differences in the risk factor profiles of patients with premature (M)INOCA compared to obstructive 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 | 0.001 |
| 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) | 0.001 | 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".