Correlation between Uric Acid and Non-Alcoholic Steatohepatitis (NASH) Occurred in Obesity and Non-Obesity
Bibliographic record
Abstract
BACKGROUND/AIM: Non-alcoholic Fatty Liver Disease (NAFLD) is one of the main causes that promote chronic liver disease in developing countries. Uric acid is correlated to metabolic syndrome. Based on this issue, we studied the correlation between uric acid level and the occurrence of NASH in non-alcoholic fatty liver disease (NAFLD) with or without obesity. METHODS: The research subjects were 149 patients diagnosed with NAFLD. The data were collected from the medical record by purposive sampling method. The subjects were taken from inpatient and outpatient data from Wahidin Sudirohusodo hospital. The medical record included demography, clinical, radiology and laboratory records. Statistical analysis were performed through descriptive statistical calculations, Pearson Correlation and multinomial logistic. RESULTS: There was a significant correlation between NAFLD and uric acid level (p=.000). Based on gender, the correlation between NAFLD and uricemia was significant in female patients (with p=.000); but insignificant in male patients (p=.137). Based on age, in age of >40 years old, NAFLD was significantly associated with uric acid level (p=.000). There was a significant correlation between hyperuricemia and NASH in obese and non-obese patients (p <0.001) for which the higher the uric acid level the greater the NAFLD degree was. CONCLUSION: There is a correlation between uric acid level and NASH occurrence in NAFLD with or without obesity.
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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 | 0.000 |
| 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".