Burden of nonalcoholic fatty liver disease in Canada, 2019–2030: a modelling study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) account for a growing proportion of liver disease cases, and there is a need to better understand future disease burden. We used a modelling framework to forecast the burden of disease of NAFLD and NASH for Canada. METHODS: We used a Markov model to forecast fibrosis progression from stage F0 (no fibrosis) to stage F4 (compensated cirrhosis) and subsequent progression to decompensated cirrhosis, hepatocellular carcinoma, liver transplantation and liver-related death among Canadians with NAFLD from 2019 to 2030. We used historical trends for obesity prevalence among adults to estimate longitudinal changes in the number of incident NAFLD cases. RESULTS: The model projected that the number of NAFLD cases would increase by 20% between 2019 and 2030, from an estimated 7 757 000 cases to 9 305 000 cases. Increases in advanced fibrosis cases were relatively greater, as the number of model-estimated prevalent stage F3 cases would increase by 65%, to 357 000, and that of prevalent stage F4 cases would increase by 95%, to 195 000. Estimated incident cases of hepatocellular carcinoma and decompensated cirrhosis would increase by up to 95%, and the number of annual NAFLD-related deaths would double, to 5600. INTERPRETATION: Increasing rates of obesity translate into increasing NAFLD-related cases of cirrhosis and hepatocellular carcinoma and related mortality. Prevention efforts should be aimed at reducing the incidence of NAFLD and slowing fibrosis progression among those already affected.
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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 it