Bibliographic record
Abstract
Introduction: Bandırma and the surrounding districts are in a geographical location with a dynamic population and a large number of immigrants due to university, industry, agriculture and trade. In this context, the coronary artery disease profile can be evaluated as a reflection of the country in general. In this study, it was aimed to determine the coronary artery disease profile of patients who underwent coronary angiography in Bandırma Training and Research Hospital, a new heart center, and the region where the hospital is located. Materials and Methods: Patients who underwent coronary angiography after the establishment of the heart center in Bandırma Training and Research Hospital were included in the study. The patients' age, gender, diagnosis, which treatment decision was made as a result of coronary angiography, stent restenosis, graft patency and coronary artery anomalies were evaluated. Results: The number of patients who underwent coronary angiography was 3238 and 1079 unstable angina pectoris, 959 stable angina pectoris, 573 non-ST elevation myocardial infarction, 325 inferior myocardial infarction, 258 anterior myocardial infarction, 37 posterior myocardial infarction, 7 lateral myocardial infarction was diagnosed. The mean age of the patients was 62±12 years, and the mean age of angiography in females was 4 years longer than males. It was observed that medical treatment was given to 1209 patients, percutaneous coronary intervention to 1630 patients, and bypass surgery to 399 patients. Conclusion: The coronary artery disease profile of Bandırma and the surrounding districts reflects the coronary artery disease profile of the country in general.
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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 | 0.001 | 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) | 0.005 | 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".