Abstract 10642: A Novel Way to Predict Obstructive Coronary Artery Disease Requiring Revascularization: The Use of Magnetocardiography
Bibliographic record
Abstract
Introduction: Coronary artery disease (CAD) causes one-third of total deaths in people over the age of 35. The evaluation of suspected obstructive CAD includes H&P, ECG, and cardiac enzymes. These tests can often lead to diagnostic uncertainty which can then lead to unnecessary testing such as stress tests and/or coronary angiography. Magnetocardiography (MCG) has been proposed as a noninvasive, radiation free, rapid adjunctive test in predicting coronary ischemia requiring revascularization. MCG measures the magnetic fields produced by electrical currents in the heart with unique and distinct advantages over traditional electrocardiographic approaches. Methods: One hundred patients (62% male, 69% white, age 60.9 years), suspected of having obstructive CAD, were evaluated at a large urban community hospital. All patients underwent MCG prior to coronary angiography which was interpreted by readers blinded to clinical data. MCG was considered positive if the scan displayed findings of “RT angle”, “multipolar”, or “island”. Of the 100 patients, 72 MCG studies were interpretable. Results: Of the 72 MCG studies, 25 were considered positive. Of the 25, 17 revealed obstructive CAD on angiography and underwent PCI with balloon or stent. Of the 47 negative, 35 revealed no obstructive CAD on angiography. Sensitivity was 58.6% and specificity was 81.4%. Removing patients with “island” MCG features resulted in 65 studies which improved specificity to 92.1% at a slight penalty in sensitivity at 55.6%. There were 11 patients where stress testing and MCG could be compared to angiography revealing a diagnostic accuracy of 54.5% and 81.8%, respectively. Discussion: MCG was able to predict obstructive CAD leading to PCI with a specificity of 81.4%. Refining interpretation criteria improved specificity to 92.1% plus better diagnostic accuracy than stress testing. MCG shows promise as an adjunctive test for patients with suspected CAD who may require revascularization with PCI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".