Impact of chronic hepatitis B and interferon‐α therapy on growth of children
Bibliographic record
Abstract
Interferon-alpha (IFN) has been approved as treatment for children with chronic hepatitis B (CHB). The aims of this study were to assess the impact on children's growth of the disease itself and of IFN treatment. The growth of 142 children with CHB (70 IFN-treated, 72 untreated) was monitored for a minimum of one year. Regression analysis models were used to determine which of the variables most affected children's growth. After adjusting for racial differences, the population of 142 children with CHB had a mean baseline height for age percentile of 39 and a mean baseline weight for age percentile of 38, which were significantly different (P < 0.0001) from the 50th percentiles of their respective reference populations. The height for age Z score of untreated children was inversely correlated with serum hepatitis B virus DNA and aspartate aminotransferase levels, and the weight for age Z score was inversely correlated with serum hepatitis B virus DNA levels. While undergoing IFN therapy, children displayed a "U-shaped" growth pattern, such that height for age and weight for age Z scores at 3 or 6 months were lower than scores at baseline or 12 months. In this study the average child with CHB showed compromised growth even in the absence of IFN therapy. During IFN therapy, children's growth was temporarily disrupted.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".