Identifying Which Treated Hypertensive Patients Without Known Coronary Artery Disease Should Be Tested for the Presence of Myocardial Ischemia by Perfusion Imaging
Bibliographic record
Abstract
Stress dipyridamole technetium-99(m) sestamibi single photon emission computed tomographic imaging was used to study myocardial perfusion in 1116 hypertensive patients without known coronary artery disease (CAD). The test confirmed the presence of CAD in 28.9% of patients. As expected, patients with diabetes mellitus (DM) had a significantly higher prevalence of myocardial perfusion abnormalities (35.9% vs 23.9%; odds ratio, 1.79; 95% confidence interval [CI], 1.38-2.33; P<.0001) and high-risk myocardial imaging (16.4% vs 10.6%; odds ratio, 1.67; 95% CI, 1.18-2.37; P=.004) than those without DM. Odd ratios further increased, again as expected, with dyslipidemia (2.19; 95% CI, 1.54-3.12; P<.0001), peripheral arterial disease (2.61; 95% CI, 1.77-3.85; P<.0001), microalbuminuria (3.03; 95% CI, 1.91-4.82; P<.0001), and abnormal electrocardiographic findings (3.06; 1.68; 95% CI, 2.08-4.48; P<.0001). This large cohort study showed that more than 1 of 4 treated hypertensive patients have subclinical CAD. These study data should be clinically helpful in selecting hypertensive patients who are the most suitable candidates to screen for the presence of CAD.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".