assosiation of non alcholic fatty liver disease with coronary artery disease
Bibliographic record
Abstract
OBJECTIVE: To determine the association between non alcoholic fatty liver disease and coronary artery disease. METHODOLOGY: This cross sectional study is conducted from July 2016 to December 2016, in cardiology unit, Lady reading hospital. By using non probability consecutive sampling, patients of all age groups and either gender, presenting to cath: lab for coronary angiography, indicated for angina CCS III, were included in the study. All patients fulfilling inclusion and exclusion criteria were subjected to screening for NAFLD by using ultrasonography. Patients were classified into having no, mild, moderate and sever NAFLD. Correlation between NAFLD and CAD, confirmed on cath: studies, was done using Spearman’s rho test. RESULTS: Total of 370 patients with mean age of 55.36 ± 10.07 years were enrolled in the study, of which 44.6% were females. Known risk factors for CAD like Diabetes mellitus, hypertension, and smoking were present in 63.5%, 64.9% and 23% respectively. 28.4% of patients had no NAFLD, 28.4% had mild, 28.4% had moderate and 14.4% had sever NAFLD. 12.2% had no CAD while mild, moderate and sever disease was present in 36.5%, 31.1% and 2.3% respectively. By using chi square test co relation co efficient between NAFLD and CAD was calculated and came out to be 285.536 ( p value <0.000). NAFLD also increased the odds of having CAD by 2.9 times with a p value for odd ratio <0.000. CONCLUSION: NAFLD is strongly associated as an independent risk factor with CAD and increases the odds of having CAD. KEY WORDS: NAFLD= Non alcoholic fatty liver disease, CAD= Coronary artery disease, CCS= Canadian classification scale, Cath:= Cardiac catheterization
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 0.001 |
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".