Abstract 13267: Angina Burden and Coronary Vascular Function in Women With Ischemia and No Obstructive Coronary Arteries—Role of Oxygenation-Sensitive Cardiovascular Magnetic Resonance Imaging
Bibliographic record
Abstract
Introduction: Patients with ischemia and no obstructive coronary arteries (INOCA) often have coronary microvascular dysfunction (CMD). An association between higher angina burden (lower Seattle Angina Questionnaire [SAQ] scores) and increased severity of CMD has previously been reported. Oxygenation-Sensitive Cardiovascular Magnetic Resonance (OS-CMR) is a novel approach that can potentially evaluate CMD. To date, the relationship between angina burden and OS-CMR biomarkers has not been elucidated. Methods: We investigated the association between angina burden, quantified by the mean and 5 individual components of the SAQ, with measures of myocardial oxygenation, using OS-CMR. More severe CMD is reflected by a decreased % signal intensity (SI) change after hyperventilation (Breathing-enhanced Myocardial REserve, B-MORE), calculated by [(Hyperventilation - Breath Hold)/Breath Hold] x 100%. Univariable linear regression analysis was used to investigate the associations. Results: In 44 women with INOCA (mean age 55.1±0.9 years), B-MORE was 5.0±1.4% (Table). Several components of the SAQ were associated with OS-CMR measures of CMD [physical limitation (R 2 =0.281, Beta=0.530, p=0.042), angina frequency (R 2 =0.216, Beta=0.465, p=0.039), disease perception (R 2 =0.254, Beta=0.504, p=0.024), and the mean overall SAQ (R 2 =0.285, Beta=0.534, p=0.015)] (Panel Figure). Conclusion: In women with INOCA, angina burden is associated with coronary vascular function, as assessed by OS-CMR. Therefore, this novel approach may provide objective information to detect the presence of CMD.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".