Management of Pediatric Nonalcoholic Fatty Liver Disease by Academic Hepatologists in Canada
Bibliographic record
Abstract
BACKGROUND: The literature on the optimal clinical management of pediatric patients with nonalcoholic fatty liver disease (NAFLD) is limited. The objective of this study was to identify discrepancies in the care provided to patients with NAFLD by hepatologists practicing in academic centers across Canada. METHODS: A nationwide survey was distributed electronically to all pediatric hepatologists practicing in university-affiliated hospitals using the infrastructure of the Canadian Pediatric Hepatology Research Group. The responses were anonymous. RESULTS: The response rate to the survey was 79%. Everyone reported diagnosing NAFLD based on a combination of elevated transaminases and imaging suggestive of steatosis in the context of an otherwise negative workup for other liver diseases. Only 14% use liver biopsy to confirm the diagnosis. There are significant discrepancies in the frequency of screening for other comorbidities (eg, hypertension, sleep apnea, etc) and in the frequency of laboratory investigations (eg, lipid profile, transaminases, international normalized ratio, etc). Frequency of outpatient clinic follow-up varies significantly. Treatment is consistently based on lifestyle modifications; however, reported patient outcomes in terms of body mass index improvements are poor. CONCLUSIONS: There are significant discrepancies in the care provided to children with NAFLD by hepatologists practicing in academic centers across Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".