Association Between Ankle-Brachial Index and Coronary Lesions Assessed by Coronary Angiography
Bibliographic record
Abstract
Background: The ankle-brachial index (ABI) is a simple, non-invasive, and inexpensive method used in the diagnosis of peripheral arterial disease (PAD) and can identify individuals at risk for cardiovascular disease in other arteries of the body, especially the coronary and carotid arteries. The primary objective of this study was to assess whether patients with an ABI < 0.9 have more severe coronary artery disease detected on coronary angiography compared to patients with a normal ABI. Methods: This is a prospective, analytical, cross-sectional study that was performed from July 1, 2013 to June 31, 2014 that recruited 163 patients (101 men (62%) and 62 women (38%)) according to the inclusion and exclusion criteria. All patients underwent coronary angiography, and then ABI measurements were performed. Pearson's Chi-square and Student's t -tests were used to compare variables between groups. The Poisson regression model was used to evaluate whether ABI was an independent predictor of stenoses > 50%. Results: The prevalence of ABI < 0.9 was 9.8%. Patients with an ABI < 0.9 had a higher prevalence of stenoses >=50% in the left anterior descendant (LAD) (68.7% vs. 36%, P = 0.02) and left main (8.7% vs. 0.6%, P < 0.001) than those with a normal ABI. On multivariate Poisson regression, an ABI < 0.9 was an independent predictor of stenosis >=50% in the LAD (odds ratio (OR): 2.05 (1.39 - 3.04), P < 0.001). Conclusions: Patients with an ABI < 0.9 had a higher prevalence of stenoses >=50% in the LAD and left main than those with a normal ABI. An abnormal ABI was an independent predictor of lesions >=50% in LAD. Cardiol Res. 2015;6(1):216-220 doi: http://dx.doi.org/10.14740/cr376w
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".