User and system pitfalls in liver imaging with LI‐RADS
Bibliographic record
Abstract
The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver imaging, created specifically for patients at risk for hepatocellular carcinoma. Over the past years, LI-RADS has been progressively implemented into clinical practice, but pitfalls remain related to user error and inherent limitations of the system. User pitfalls include the inappropriate application of LI-RADS to a low-risk patient population, incorrect measurement techniques, inaccurate assumptions about LI-RADS requirements, and improper usage of LI-RADS terminology and categories. System pitfalls include areas of discordance with the Organ Procurement and Transplantation Network (OPTN) as well as pitfalls related to rare ancillary features. This article reviews common user pitfalls in applying LI-RADS v2018 and how to avoid preventable errors and also highlights deficiencies of the current version of LI-RADS and how it might be improved in the future. Level of Evidence:3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2019;50:1673-1686.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".