Screening Patterns of Nonalcoholic Fatty Liver Disease in Children with Obesity in Canadian Primary Care: A Cross-Sectional Study
Bibliographic record
Abstract
Background. Nonalcoholic fatty liver disease (NAFLD) is the most common pediatric chronic liver disease, and children with a body mass index (BMI) ≥95th percentile are recommended to be screened for NAFLD by liver enzymes. Objectives. This study aimed to determine the frequency and predictors of screening for NAFLD among children with obesity in Canada and to evaluate a sample of children with suspected NAFLD. Methods. This cross-sectional study used data from the Canadian Primary Care Sentinel Surveillance Network, a repository of electronic medical record data from Canadian primary care practices. Results. Of n = 110,827 children aged 9–18 years, 13.9% (n = 9,888) had a BMI ≥95th percentile. Only 8.7% (n = 859) of these patients were screened for NAFLD in the last year, and 23.6% (n = 2336) were ever screened. Using logistic regression, screening in the last year was associated with demographic and clinical characteristics, including previous liver enzyme assessment, prior antidiabetic prescription, and prior anxiolytic prescription. Among children with suspected NAFLD (n = 1,046), 34.7% had a BMI ≥99th percentile and approximately 8% were at increased risk of significant liver disease. Conclusion. The study revealed low screening rates for NAFLD in Canadian primary care and highlighted the important role of primary care providers in identifying and managing pediatric NAFLD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".