Multiple Risk Factors of Alcoholic and Non-Alcoholic Myocardial Infarction Patients
Bibliographic record
Abstract
BACKGROUND: Myocardial infarction (MI) is one of the most critical medical emergency and contributor to morbidity and mortality worldwide. Myocardial infarction is the most common form of coronary heart disease and leading cause of premature death. Past century has seen substantial advancement in the field of medical sciences but still mortality trends due to myocardial infarction is increasing in developing countries including India. We have conducted this study to compare the Sociodemographic characteristics of alcoholic and non alcoholic MI patients admitted in coronary care unit of Saveetha Medical College, Chennai, India. METHODS: An exploratory cross sectional study was performed by enrolling a convenient sample of 100 Myocardial Infarction patients. Information about Sociodemographic characteristics, past medical history, alcohol and tobacco intake, physical activity, psychological stress and biochemical measurements was gathered. RESULTS: The mean age of the respondents was 46 (SD=6) years and majority of them were male i.e. 82%. 100% married and 89% literate, there were 24% past and 22% present alcoholics. Consumption of alcohol on a monthly, weekly and daily basis was 8%, 11% and 5% respectively. Preference to brandy was 67%, rum was 21% and that the beer was 12%. Current smoker were 20% and former were 11%. 93% and 52% respondents were under medication of beta blocker and angiotensin-converting-enzyme (ACE) inhibitors respectively. CONCLUSION: Worldwide, MI is the most common cause of mortality and morbidity and hence early diagnosis and management is most essential. Results from our study revealed that, participants had sedentary lifestyles where risk factors of MI such as alcohol consumption, and smoking does existed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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".