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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".