Assessment of Growth and Development in Children With Hepatitis B Positivity
Bibliographic record
Abstract
BACKGROUND: Chronic infections and liver diseases may influence the growth and development of children by leading to malnutrition. In this study, demographic characteristics, anthropometric measurements and laboratory findings for children with hepatitis B positivity were analyzed. METHODS: A total of 43 cases were admitted to our clinic between January 2012 and February 2013 and detected to have HBsAg positivity. RESULTS: Malnutrition was detected in 11 cases (25.6%) and obesity in three cases (6.9%). Aspartate aminotransferase (AST) levels were significantly higher in malnourished patients compared to those without malnutrition. The weight to height was significantly higher in patients with positive HBeAg compared to children with negative HBeAg. We found that the weight standard deviation scores (SDS) ratios dropped as alanine aminotransferase (ALT) and AST levels increased and height SDS ratios decreased. In addition, body mass index (BMI) decreased as AST and alpha feto protein (AFP) values increased. While a significant relationship was not detected between insulin-like growth factor binding protein-3 (IGFBP-3) and insulin-like growth factor-1 (IGF-1) and ALT, a significantly negative correlation was detected between IGFBP-3 and IGF-1 and AST. We found a malnutrition rate of 25.6% in children with HBsAg positivity. We also found that weight and height SDS rates decreased as ALT and AST levels increased. In addition, we detected that BMI decreased as AST and AFP values increased. CONCLUSION: We consider that hepatic inflammation is the factor that affects growth. Monitoring of growth and development during follow-up of children who are detected to have HBsAg positivity would be beneficial to determine the mechanism and causes of growth retardation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".