Do Women With Anxiety or Depression Have Higher Rates of Myocardial Ischemia During Exercise Testing Than Men?
Bibliographic record
Abstract
BACKGROUND: Women diagnosed with coronary artery disease (CAD) typically experience worse outcomes relative to men, possibly through diagnosis and treatment delays. Reasons for these delays may be influenced by mood and anxiety disorders, which are more prevalent in women and have symptoms (eg, palpitations and fatigue) that may be confounded with CAD. Our study examined sex differences in the association between mood and anxiety disorders and myocardial ischemia in patients with and without a CAD history presenting for exercise stress tests. METHODS AND RESULTS: A total of 2342 patients (women n=760) completed a single photon emission computed tomographic exercise stress test (standard Bruce Protocol) and underwent a psychiatric interview (The Primary Care Evaluation of Mental Disorders) to assess mood and anxiety disorders. Ischemia was assessed using single photon emission computed tomography, with odds ratio used to calculate the effect of sex and mood/anxiety on the presence of ischemia during stress testing by CAD history in a stratified analyses, adjusted for relevant covariates. There was a sex by anxiety interaction with ischemia in those without a CAD history (P=0.015): women with anxiety were more likely to exhibit ischemia during exercise than women without anxiety (odds ratio, 1.75; 95% confidence interval, 1.05-2.89). No significant effects were observed for men nor mood. CONCLUSIONS: Women with anxiety and no CAD history had higher rates of ischemia than women without anxiety. Results suggest that anxiety symptoms, many of which overlap with those of CAD, might mask CAD symptoms among women (but not men) and contribute to referral and diagnostic delays. Further research is needed to confirm this hypothesis.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".