Incidence of Anomalous Coronary Arteries in Pakistani Population
Bibliographic record
Abstract
Objective: To identify the origin of coronary arteries and detect its abnormalities. Methodology: This was a retrospective analysis comprising 1200 patients during the course of 2 years from May 2021 to a May 2023 who underwent for coronary CT angiogram to determine the coronary artery anomalies at the Islamabad Diagnostician center, with the collaboration of South East Hospital and Research Center, Islamabad. All the patients who were included in this study had a base line heart rate>65 bpm, and suffered from coronary artery disease and chest pain. They were advised to avoid smoking and coffee 12 hours before the procedure and also avoided eating solid food 4 hours prior to the procedure. Results: The patients' mean age was 49 years, mean heart rate 61.39±5.356 and mean creatinine level was 1.029±1.383. It also showed that 85.7% male and only 2% females were in records. Medical history showed that 64.3% patients were hypertensive, 50% diabetic, 92% patients had presented with shortness of breath, 85% with chest pain, 35.7% were smokers and 42.9% had positive family history of heart diseases. Angina grading score also known as the Canadian cardiovascular society (CCS) showed 50% patients with CCS I, 28.6% CCS II and 21.4% CCS III. Conclusion: Coronary CT angiography is a highly effective diagnostic technique for the diagnosis and origin of coronary arteries as well as its course and termination. We can observe easily all the presentation which cannot be detected through other diagnostic tools.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".