Part 2: CAR Metabolic Dysfunction-Associated Steatotic Liver Disease Working Group Recommendations for Risk Stratifying Patients With MASLD
Bibliographic record
Abstract
The Canadian Association of Radiologists (CAR) metabolic dysfunction-associated steatotic liver disease (MASLD) Working Group (WG) is a multidisciplinary working group composed of radiologists, hepatologists, and family physicians. In this 3-part series, we provide Delphi consensus-based guidance on clinical and imaging findings for patients with known or suspected MASLD (formerly termed nonalcoholic fatty liver disease or NAFLD). Part 1 focuses on the detection and grading of hepatic steatosis on imaging; Part 2 on risk-stratification of patients with MASLD, including a patient pathway using serological and imaging investigations; and Part 3 on the implementation of practice recommendations for quality assurance using shear wave elastography (SWE) and magnetic resonance elastography (MRE) programs. In Part 2 of these guidelines, the WG provides 14 recommendations for risk stratifying MASLD patients into low and high risk for advanced hepatic fibrosis ( F ≥ 3) using a combination of serological and imaging investigations. Population screening and risk stratification of MASLD patients using a clinical and serological investigation (FIB-4) followed by imaging pathway is recommended. Clinical, technical, and grading recommendations for point shear wave elastography (pSWE), 2-dimensional SWE (2D-SWE), and MRE are discussed.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".