Primary Headache and Silent Myocardial Ischemia in Patients with Coronary Artery Disease
Bibliographic record
Abstract
OBJECTIVE: The mechanisms by which migraine is linked to ischemic vascular disease remain uncertain and are likely to be complex. The aim of this study was to investigate the correlation between silent myocardial ischemia (SMI) and a history of documented primary headache in a large population of patients with exercise-induced myocardial ischemia. METHODS: The study involved 1,427 consecutive patients (918 symptomatic and 509 asymptomatic patients) with exercise-induced myocardial ischemia and documented coronary artery disease (CAD). RESULTS: Patients with anginal symptoms during exercise-induced myocardial ischemia had a significantly higher prevalence of primary headache than those without (41 vs. 30%, p < 0.001). Patients with angina pectoris in daily life also had greater prevalence of primary headache than those without anginal symptoms (37 vs. 20%; p < 0.0001). Symptomatic patients during percutaneous transluminal coronary angiography or myocardial infarction had a greater prevalence of primary headache than asymptomatic patients (p < 0.001 and p = 0.005, respectively). CONCLUSIONS: Our data suggest that a history of headache in CAD population is correlated to a high probability of anginal symptoms and a decreased probability of SMI. The anamnestic absence of headache requires a close monitoring for patients with risk factors for CAD, because this population seems to have a lower susceptibility to pain and the risk of developing SMI might be increased.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".