Current considerations for clinical management and care of non-alcoholic fatty liver disease: Insights from the 1st International Workshop of the Canadian NASH Network (CanNASH)
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) affects approximately 8 million Canadians. NAFLD refers to a disease spectrum ranging from bland steatosis to non-alcoholic steatohepatitis (NASH). Nearly 25% of patients with NAFLD develop NASH, which can progress to liver cirrhosis and related end-stage complications. Type 2 diabetes and obesity represent the main risk factors for the disease. The Canadian NASH Network is a national collaborative organization of health care professionals and researchers with a primary interest in enhancing understanding, care, education, and research around NAFLD, with a vision of best practices for this disease state. At the 1st International Workshop of the CanNASH network in April 2021, a joint event with the single topic conference of the Canadian Association for the Study of the Liver (CASL), clinicians, epidemiologists, basic scientists, and community members came together to share their work under the theme of NASH. This symposium also marked the initiation of collaborations between Canadian and other key opinion leaders in the field representative of international liver associations. The main objective is to develop a policy framework that outlines specific targets, suggested activities, and evidence-based best practices to guide provincial, territorial, and federal organizations in developing multidisciplinary models of care and strategies to address this epidemic.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".