What is the prevalence of hepatic steatosis on ultrasonography in patients followed for nephrolithiasis?
Bibliographic record
Abstract
INTRODUCTION: Patients with non-alcoholic fatty liver disease (NAFLD) have higher prevalence of nephrolithiasis. The aim of the present study was to determine prevalence of hepatic steatosis on ultrasonography in nephrolithiasis patients. METHODS: Charts of 318 consecutive nephrolithiasis patients seen in stone clinic between January and February 2018 were retrospectively reviewed. Ultrasound reports were reviewed for hepatic steatosis. Subsequent liver investigations were noted. Patients' demographic predictors of hepatic steatosis were identified using univariable logistic regression models. RESULTS: A total of 162 patients was included, of which 76 (46.9%) were found to have hepatic steatosis and 22 (13.6%) were found to have moderate-to-severe hepatic steatosis. Median followup was 2.03 years. Predictors of hepatic steatosis included higher body mass index and smoking (both p<0.05). Progression of fatty liver on ultrasound was noted for 13 (17.1%) and regression was noted for two (2.6%). Of the 16 patients with a Fibrosis-4 (FIB-4) score, four (25.0%) patients required further investigation and 12 (75.0%) were unlikely to have advanced fibrosis. Of 12 patients who underwent fibroscan, one (8.3%) had both fibrosis and cirrhosis, two (16.7%) fibrosis only, and two (16.7%) moderate-to-severe steatosis. CONCLUSIONS: Hepatic steatosis on ultrasound followup of nephrolithiasis patients is common, especially in smokers and overweight patients. Current recommendations suggest that primary care physicians calculate a FIB-4 score upon the detection of hepatic steatosis on ultrasound. The decision to refer to hepatology for a corroborative fibroscan is then based on the FIB-4 score.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".