Coronary Angiography by CT Accuracy for the Chronic Coronary Disease Diagnosis
Bibliographic record
Abstract
Objective: The main cause for ischemic heart disease is the coronary atherosclerosis (1,2). The positive diagnosis for stable ischemic heart disease starts with the probability of obstructive coronary atherosclerotic. This study sought to investigate the clinical, biological and imaging characteristics associated with diagnostic accuracy of detecting obstructive coronary artery disease defined by coronary angiography result. Methods: We screened patients underwent angiography by computed tomography. Thru the post processing analysis we obtain the Agatston calcium score, the presence, the magnitude and the localization of significant atherosclerotic coronary lesions. Results: We enrolled 133 patients. The calcium score and the presence of atherosclerotic coronary lesions was significantly different between men and woman. The presence of any isolated cardiovascular risk factor wasn?t associated with atherosclerotic coronary artery disease, but all the three available scores of prediction had positive correlation with the calcium score and the presence of the coronary lesions. Conclusions: The best method to predict the presence of coronary atherosclerotic lesions was the Canadian Society of Cardiology Probability Score, which has the most complex pretest evaluation taking into consideration several cardiovascular risk factors. Elevated calcium score and the number of atherosclerotic coronary involvement at the angiography by computed tomography correlated with performing coronarography, with the positive diagnosis at invasive angiography and with angioplasty and stent placement. Evaluation thru angiography by computed tomography is an accurate method, and should be used for patients with medium-high pretest probability of ischemic heart disease and not for patients with isolated cardiovascular risk factors.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".