Effect of Biologic Treatments on Growth in Children with Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Growth retardation is a frequent complication of severe juvenile idiopathic arthritis (JIA). Biologic treatments may improve growth velocity by controlling systemic inflammation and reducing corticosteroids. Our goals were to compare growth velocity before and after the onset of biologic therapy and to determine whether the JIA subtype, the use of steroids, the requirement of one or several biologic agents, or the disease activity influenced growth velocity. METHODS: We retrospectively analyzed the growth of children with JIA who never received growth hormone treatment, who started biologic treatment before puberty, and who were followed for at least 6 months afterward. RESULTS: We included 100 children (33 boys). Median patient age was 7.1 years (range: 1.6-15.7) at the onset of biologic treatment and 11.0 years (range: 2.3-19.5) at the latest followup. Forty-six patients had received corticosteroid and 34 had received more than 1 biologic agent. Patient median height expressed as SD score (SDS) was 0.31 (range: -2.47 to 5.46) at disease onset, -0.24 (-3.63 to 2.90) at biologic therapy onset (p < 0.0001), and -0.15 (-4.95 to 3.52) at the latest followup (p = 0.171 compared to biologic treatment onset). Patients who required several biologics and systemic patients had a significantly lower growth velocity after the onset of biologic treatment. At the latest followup, 18% of our study group had low growth velocities and 19% were below -2SD or shorter than genetically programmed. CONCLUSION: In a subset of patients, particularly systemic JIA patients and patients who required more than 1 biologic, biologic therapy may be insufficient to restore normal growth velocity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".