Prevalence and severity of MASLD and fibrosis using transient elastography: A cross-sectional screening in Lower Mainland, British Columbia, Canada
Bibliographic record
Abstract
Background: Metabolic dysfunction–associated steatotic liver disease (MASLD) is a leading cause of liver disease worldwide. We aimed to assess the prevalence and severity of MASLD and fibrosis among asymptomatic individuals with no known history of liver disease in British Columbia (BC), Canada. Methods: We conducted a cross-sectional, population-based screening study of 2,782 individuals in the Lower Mainland of BC. Baseline demographics were collected, and transient elastography was performed. The prevalence and severity of MASLD and liver fibrosis were calculated. Results: MASLD affected 53.1% of participants, with 34.0% having severe hepatic steatosis, 6.5% moderate hepatic steatosis, and 12.6% mild hepatic steatosis. Factors associated with a higher MASLD incidence included non-lean BMI (OR 5.50, p <0.001), hypertension (OR 1.29, p = 0.014), diabetes (OR 1.33, p = 0.026), and South Asian ethnicity (OR 1.36, p = 0.014), while female gender was protective (OR 0.81, p = 0.015). Non-lean BMI (OR 5.71, p <0.001), hypertension (OR 1.33, p = 0.002), and diabetes (OR 1.34, p = 0.010) were associated with more severe steatosis. Fibrosis was present in 7.2% of participants, with 4.4% having moderate fibrosis, 1.9% having severe fibrosis, and 0.9% having cirrhosis. Diabetes (OR 1.93, p <0.001) and non-lean BMI (OR 2.37, p <0.001) were associated with a higher prevalence of fibrosis, while East Asian ethnicity was protective (OR 0.50, p <0.001). Non-lean BMI (OR 2.35, p <0.001) and diabetes (OR 1.96, p <0.001) were linked to higher fibrosis severity, while East Asian ethnicity remained protective against severe fibrosis (OR 0.50, p <0.001). Conclusions: There is a significant burden of liver steatosis and fibrosis in BC, Canada, which highlights the need for comprehensive MASLD screening guidelines.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".