A Canadian survey on knowledge of non-alcoholic fatty liver disease among physicians
Bibliographic record
Abstract
BACKGROUND: In Canada, non-alcoholic fatty liver disease (NAFLD) is the most frequently occurring liver disease, affecting one in four Canadians. NAFLD can in turn evolve into non-alcoholic steatohepatitis (NASH) and cirrhosis. No study in Canada has investigated knowledge of NAFLD among physicians. METHODS: Primary care physicians (PCPs); specialists in internal medicine, gastroenterology, and hepatology; and hepatology nurses who were members of the College of Family Physicians of Canada, Canadian Association for the Study of the Liver, or Canadian Association of Hepatology Nurses were invited to participate in this web-based survey. RESULTS: Of 650 invited physicians and nurses, 214 (33%) responded and 171 (26%) completed the whole survey. Overall, 51% of the respondents were PCPs, 38% were specialists, and 11% were nurses. Of these, 58% of PCPs, 28% of specialists, and 39% of nurses responded that they were only somewhat familiar or unfamiliar with NAFLD. Moreover, 53% of PCPs, 20% of specialists, and 35% of nurses thought the prevalence of NAFLD in Canada was 15% or less. Also, 42% of respondents thought that NASH could be diagnosed by imaging or blood tests. Finally, more than 40% of PCPs, 22% of specialists, and 33% of nurses thought that metformin and statin were treatments for NASH. CONCLUSIONS: This survey shows that a significant proportion of Canadian physicians and nurses managing patients with NAFLD are not very familiar with the disease. This study emphasizes the need for further provider education, national practice guidelines, and improved treatment options.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".