Burden of nonalcoholic fatty liver disease in Canada, 2019–2030: a modelling study
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".