Breaking boundaries: Unraveling metabolic dysfunction-associated steatotic liver disease in children of India and Canada
Bibliographic record
Abstract
Background: Non-alcoholic fatty liver disease (NAFLD) or metabolic dysfunction-associated steatotic liver disease (MASLD) is a major cause of chronic liver disease in children. Its prevalence is rising globally, yet it is uncertain if its onset and severity vary between countries. We aimed to compare pediatric NAFLD in two Canadian and Indian tertiary care centers. Methods: This study was conducted as a retrospective cohort study and patient related details were retrieved from the electronic records and reviewed. Results: The study analyzed a total of 184 children with NAFLD/MASLD (94 from the Indian site and 89 from the Canadian site) with concordance between NAFLD and MASLD definitions. The Indian children had a higher proportion of symptomatic presentations and family history of metabolic disorders ( p = 0.0001) while the Canadian children had higher median weight, BMI, blood pressure, and waist circumference ( p < 0.05). Indian children had higher hepatic transaminases and low density lipoprotein levels, while the Canadian site had higher serum insulin, blood glucose, homeostasis model assessment of insulin resistance, high density lipoprotein cholesterol levels, liver stiffness, and controlled attenuation parameter values ( p < 0.05). Majority (78%) of the Canadian children who underwent liver biopsy had significant fibrosis (>stage 2). In the overall cohort, waist circumference could be identified as an independent risk factor, irrespective of country of origin, predicting hepatic fibrosis. Conclusions: The study found significant differences between cohorts. Canadian children showed higher obesity grades and greater hepatic steatosis and fibrosis severity. To comprehend the underlying causes, future studies are imperative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".