A201 PREVALENCE OF NONALCOHOLIC FATTY LIVER DISEASE IN A COHORT OF HEALTHY CHILDREN IN ONTARIO
Bibliographic record
Abstract
Abstract Aims To determine the prevalence of hepatic steatosis in a cohort of previously healthy children and adolescents, with the use of abdominal computer tomography (CT). Methods Retrospective review of the Sickkids trauma database from (year 2004 to 2015). Inclusion criteria: previously healthy children ages 1-17yr having undergone an abdominal CT scan as a part of routine trauma assessment. Exclusion criteria: Involvement of spleen and/or liver in the injury, known metabolic condition, concurrent use of medications that could predispose to hepatic steatosis. Steatosis was defined as a liver spleen attenuation index (L/S AI) of <0.8. Anthropometrics and baseline demographics were collected. Statistical analyses were performed using Graphpad Prism 7. Results A total of 143 children were included in the analyses with a mean (SD) age of 7.12(4.26) years. 88 children were male (7.10(4.24) years old). The mean (SD) weight z score was 0.48 (0.97). Fifteen percent of the 137 children with a weight z-score between -2 to 2 had evidence of hepatic steatosis on imaging (using liver/spleen index <0.8). Twenty seven percent (6/22) of children with weight z-scores less than the 33rd percentile had L/S AI <0.8. An inverse correlation between weight z-scores and L/S AI was found in children >10 years (P<0.016) (Fig 1); however there was no correlation in pre-pubertal children (P<0.374). Conclusions Hepatic steatosis is highly prevalent in this cohort of healthy children in Ontario. Larger population based studies are needed to investigate the prevalence of hepatic steatosis in children across Canada. Funding Agencies None
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".