Relationship between carotid intima-media thickness and coronary angiographic findings: a prospective study
Bibliographic record
Abstract
BACKGROUND: Since cardiovascular diseases are associated with high mortality and generally undiagnosed before the onset of clinical findings, there is a need for a reliable tool for early diagnosis. Carotid intima-media thickness (CIMT) is a non-invasive marker of coronary artery disease (CAD) and is widely used in practice as an inexpensive, reliable, and reproducible method. In the current study, we aimed to investigate prospectively the relationship of CIMT with the presence and extent of significant coronary artery narrowing in patients evaluated by coronary angiography for stable angina pectoris. METHODS: One hundred consecutive patients with stable angina pectoris and documented ischemia on a stress test were included in the study. The patients were divided into two groups according to the result of the coronary angiography: group 1 (39 patients) without a noncritical coronary lesion, and group 2 (61 patients) having at least one lesion more than 50% within the main branches of the coronary arteries. All of the patients underwent carotid Doppler ultrasound examination for measurement of the CIMT by a radiologist blinded to the angiographic data. RESULTS: The mean CIMT was 0.78 +/- 0.21 mm in Group 1, while it was 1.48 +/- 0.28 mm in Group 2 (p = 0.001). The mean CIMT in patients with single vessel disease, multi-vessel disease, and left main coronary artery disease were significantly higher compared to Group 1 (1.2 +/- 0.34 mm, p = 0.02; 1.6 +/- 0.32 mm, p = 0.001; and 1.8 +/- 0.31 mm, p = 0.0001, respectively). Logistic regression analysis identified CIMT (OR 4.3, p < 0.001) and hypertension (OR 2.4, p = 0.04) as the most important factors for predicting CAD. CONCLUSIONS: The findings of this study show that increase in CIMT is associated with the presence and extent of CAD. In conclusion, we demonstrated the usefulness of carotid intima-media thickness in predicting coronary artery disease but large-scale studies are required to define its role in clinical practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".