Children with autoimmune hepatitis receiving standard-of-care therapy demonstrate long-term obesity and linear growth delay
Bibliographic record
Abstract
BACKGROUND: Standard-of-care therapy in children with autoimmune hepatitis (AIH) includes induction with prednisone 1-2 mg/kg daily with gradual weaning of the dose. We aimed to test the hypothesis that children with AIH receiving standard-of-care treatment have altered growth trajectories. METHODS: Children diagnosed with AIH between 1997 and 2023 at SickKids had serial growth measurements. Mixed effect models assessed the impact of time and daily steroid exposure on z-scores. Kaplan-Meier survival methods were used to estimate the cumulative incidence of new-onset growth impairments. A time-dependent Cox proportional hazards model was constructed to determine predictors for growth impairments. RESULTS: Sixty-one children (66% females, median age at diagnosis 11.5 y) were included. BMIz showed a sharp increase, and HAZ declined significantly without returning to baseline. Each 1 mg/kg/d prednisone exposure increased BMIz gain in the first 6 months by 0.27 ([95% CI: 0.11, 0.42], p = 0.001), and decreased HAZ by -0.02 ([95% CI: -0.03, -0.01], p = 0.005). Children diagnosed before puberty exhibited a higher occurrence of excessive weight gain (72.2% vs. 49.3%; log-rank p < 0.01) and obesity (63% vs. 31.5%; log-rank p < 0.01) compared to those diagnosed during puberty. In a Cox proportional-hazards model, young age at diagnosis and daily prednisone dose >10 mg 6 months after diagnosis were predictors for linear growth delay. CONCLUSIONS: This study demonstrates that children with AIH receiving standard-of-care therapy demonstrate altered growth trajectories, long-term excess weight gain, obesity, and linear growth delay. Young age at diagnosis and >10 mg of daily prednisone at 6 months are predictors for linear growth delay. These data indicate the need to re-evaluate standard treatment algorithms for pediatric AIH in terms of steroid dosing and potential nonsteroid alternatives.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".