Screening for Prevalence of Abdominal Aortic Aneurysm During Transthoracic Echocardiography in Patient With Significant Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: Prevalence of abdominal aortic aneurysm (AAA) has ethnic differences, and coronary artery disease (CAD) shares several risk factors with AAA. Sparse Indian data are available on this. We evaluated the prevalence of AAA during transthoracic echocardiography (TTE) and risk factors of AAA in patients with CAD. METHODS: This was a prospective observational study carried out in the cardiology department at a tertiary care center from January 1, 2017 to November 30, 2017. All patients with CAD/acute coronary syndrome (ACS) were included in the study, and patients with AAA due to other etiology were excluded. Screening for an AAA was performed directly using an echocardiographic 3.5-MHz cardiac probe. RESULTS: A total of 526 patients were screened; and AAA was present in 25 (4.8%) of CAD patients. Smoking, hypertension and hyperlipidemia were predominant risk factors for AAA in our study, but were not statistically significant because same risk factors were also prevalent in the comparison group. Diabetes, peripheral vascular disease and family history were statistically significant risk factors for AAA in our study. The mean size of AAA was 34 mm. CONCLUSIONS: Presence of AAA is significantly higher among CAD patients. CAD shares several risk factors with AAA. Therefore, opportunistic examination of the abdominal aorta during routine TTE could be an effective way of screening. Diabetes mellitus, peripheral artery disease and family history were the significant associated risk factors of AAA in CAD patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".