Yield of Cardiac Magnetic Resonance Imaging in Patients With Acute Coronary Syndrome and No Obstructive Coronary Artery Disease
Bibliographic record
Abstract
PURPOSE: Ten percent to 25% of women and 6%-10% of men with acute coronary syndrome (ACS) are found to have no obstructive coronary artery disease (CAD) on coronary computed tomography angiogram or invasive coronary angiography. The etiology of presentation is often unclear. We examined the diagnostic yield of cardiac magnetic resonance imaging (CMR) in patients with signs and symptoms suggestive of an ACS and no obstructive CAD. METHODS: We retrospectively studied patients with signs and symptoms suggestive of an ACS and no obstructive CAD on coronary computed tomography angiogram or invasive coronary angiography who had CMR performed at St. Paul's Hospital, British Columbia, Canada, from 2013 to 2015. No obstructive CAD was defined as <50% stenosis in any epicardial artery. We compared CMR diagnostic yield in troponin-positive and troponin-negative patients and determined the etiology of presentation in each category. We also examined gender differences. RESULTS: Ninety-eight patients met inclusion criteria. The average age was 55.8 years, 70% were female, and 60% were troponin positive upon presentation. Abnormal CMR was observed in 35.7% of patients, yielding a diagnosis in 27.9% of females and 53.5% of males (P = 0.02). Troponin-positive patients had a significantly higher prevalence of an abnormal CMR than did troponin-negative patients (44.1% vs. 23.1%; P = 0.03). Myocarditis was more common in troponin-positive patients (25.4% vs. 2.6%; P = 0.002). CONCLUSIONS: Forty-four percent of patients with positive troponin and with signs and symptoms suggestive of an ACS, no obstructive CAD on invasive coronary angiography or coronary computed tomography angiogram, and unclear diagnosis had abnormalities on CMR that identified the diagnosis. CMR should be considered in patients with positive troponin values when the etiology for their presentation is unclear.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".