Which Method is Superior in the Diagnosis of Nonalcoholic Fatty Liver and Steatohepatatis in Children?
Bibliographic record
Abstract
Context: Nonalcoholic fatty liver disease (NAFLD) is increasing with the increased rate of obesity and reduced physical activity in children worldwide. Despite high prevalence of the disease, a standard and acceptable diagnostic method is not available. The current study aimed at collecting all related articles and evaluating the challenges. Methods: The current study searched Scopus, Web of Science, and PubMed. Articles and guidelines in English in the field of invasive and noninvasive diagnostic methods for NAFLD and nonalcoholic steatohepatitis (NASH) in children and adolescents up to Oct 2016 were used. It was tried to evaluate all laboratory and radiologic methods, biomarkers, and scores in addition to mention the challenges. Results: Ultrasonography and laboratory evaluation, which were routine methods in early diagnosis, did not have enough accuracy in this field. Diagnosis of steatosis and fibrosis and determining the severity of disease were achieved by fibro scan and controlled attenuation parameter (CAP) without the challenges of computed tomography (CT) scan and magnetic resonance imaging (MRI). Fatty liver can be predicted with high accuracy by body analyzer, anthropometric, and DEXA methods. Conclusions: Diagnosis and prediction of fatty liver should be done in all children with obesity aged > 3 years, and physician should seek the genetic and metabolic causes in children aged < 3 years and/or without overweight.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".