US LI-RADS Visualization Score: Interobserver Variability and Association With Cause of Liver Disease, Sex, and Body Mass Index
Bibliographic record
Abstract
Objectives: To evaluate the interobserver agreement between radiologists using the Ultrasound Liver Reporting And Data System (US LI-RADS) visualization score and assess association between visualization score and cause of liver disease, sex, and body mass index (BMI). Methods: This retrospective, single institution, cross-sectional study evaluated 237 consecutive hepatocellular carcinoma surveillance US examinations between March 4, 2017 and September 4, 2017. Five abdominal radiologists independently assigned a US LI-RADS visualization score (A, no or minimal limitations; B, moderate limitations; C, severe limitations). Interobserver agreement was assessed with a weighted Kappa statistic. Association between US visualization score (A vs B or C) and cause of liver disease, sex, and BMI (< or ≥ 25 kg/m 2 ) was evaluated using univariate and multivariate analyses. Results: The average weighted Kappa statistic for all raters was 0.51. A score of either B or C was assigned by the majority of radiologists in 148/237 cases and was significantly associated with cause of liver disease ( P = 0.014) and elevated BMI ( P < 0.001). Subjects with viral liver disease were 3.32 times (95% CI: 1.44-8.38) more likely to have a score of A than those with non-alcoholic steatohepatitis ( P = 0.007). The adjusted odds ratio of visualization score A was 0.249 (95% CI: 0.13-0.48) among those whose BMI was ≥25 kg/m 2 vs. BMI < 25 kg/m 2 . Conclusion: Interobserver agreement between radiologists using US LI-RADS score was moderate. The majority of US examinations were scored as having moderate or severe limitations, and this was significantly associated with non-alcoholic steatohepatitis and increased BMI.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".