Bibliographic record
Abstract
BACKGROUND: Atherosclerosis of coronary arteries is the most common cause of myocardial infarction (MI), which is initiated from childhood and progresses gradually by aging. Several risk factors influence its progress, and are categorized as classic, traditional and novel factors. The role of unknown risk factors is becoming increasingly more significant recently. The aim of this study is to underscore the novel risk factors despite the importance of classic factors and consider these factors for future studies. METHODS: This is a prospective study on 180 myocardial infarction cases, conducted in the cardiology ward and CCU of Imam-Reza hospital (Mashad-IRAN). A number of risk factors identified and evaluated in these patients included: hyperlipidemia, hypertension, diabetes, smoking, activity, stress, hair of external ear canal and ear lobe crease, age, and sex. Then patients without any risk factor or with one or two risk factors were distinguished. RESULTS: The majority of our patients were old men in the age range of 60 - 69 years. Amongst all patients 42.2% were smokers, 68.3% were type A personality group, 19% were active, 81% were physically inactive, 37.2% had hairy ear canal, 35% had hypertension, 21.1% were diabetic, 14.4% had hyperlipidemia and 30% had positive family history of myocardial infarction. Of great interest was the fact that of the patients whose case was studied, many did not have any risk factor or in some cases had only one. CONCLUSIONS: In regard of increasing rate of cardiovascular diseases and myocardial infarction even amongst the young population, and because of considerable need to improve vascular risk detection, much research over the past decade has focused on identification of novel atherosclerotic risk factors, and some of these new risk factors are identified and some may be unknown. Amongst the new risk factors, inflammation has an important role, other risk factors that must be assessed are homocysteine, serum amyloid, and antibodies against Oxidized LDL. So we recommend that governments and heart associations must introduce new plans and policies in order to tackle the problem and reduce the frequency of cardiovascular disease. This requires the understanding of the conventional or classic risk factors and also the less known and new risk factors and ways which they may be prevented.
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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 | 0.004 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".