Revisiting the Hepatorenal Index in the Quantification of Hepatic Steatosis: How it is done and the utility
Bibliographic record
Abstract
Nonalcoholic fatty liver disease (NAFLD) is a global health concern identified initially in 1980 by Ludwig, Viggiano, McGill, and Oh ( Clin Liver Dis . 2018;22:11–21) and, as of 2019, accounted for 25%–30% of the global population. NAFLD is associated with several metabolic health conditions and is characterized by fat accumulation in the liver, otherwise known as hepatic steatosis. Fat in the liver can be quantified using noninvasive imaging such as magnetic resonance imaging, computed tomography, and ultrasound. Hepatorenal index (HRI) is an ultrasound-based technique that compares the ratio of the echogenicity of the liver and the kidney. This literature review aimed to determine the utility of the HRI measurement in quantifying hepatic steatosis. Methods Twenty-three peer-reviewed articles on HRI measurements published between 2018 through 2023 were reviewed, and 11 were selected based on common subjects. The search terms included “hepatorenal index,” “HRI,” “HRI ultrasound,” “hepatorenal ultrasound index,” and “HRI ultrasound measurement.” Three common subject areas were identified in the literature and synthesized down to 11 articles. The common subjects identified were HRI technique, HRI limitations, and HRI diagnostic accuracy. The matrix provided a quick overview of the general information in each piece, aiding in the paper's overall organization. Thirteen articles were rejected as not relevant or out of date. The research question leading this review was, “What does the literature say about the value of HRI in determining moderate to severe hepatic steatosis?” Results The literature revealed that HRI could be valuable in determining moderate to severe hepatic steatosis. HRI could not accurately determine normal or mild steatosis and has several limitations. Conclusions HRI is a more objective method for determining the degree of hepatic steatosis compared with traditional B-mode ultrasound scoring and does not require additional or specialized equipment. Many studies excluded patients with various liver diseases, which may not make HRI a practical tool for clinical usefulness. Further studies should be conducted with larger patient cohorts, a greater degree of hepatic steatosis, and determine specific standardized cutoff values.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".