Effects of Juvenile Idiopathic Inflammatory Myopathies on Growth, Development, and Maturation: A Systematic Review and Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: Juvenile idiopathic inflammatory myopathies (JIIM) are autoimmune conditions that cause skin and muscle inflammation. This inflammation is thought to cause insulin resistance and disrupt various hormone axes, which may cause endocrine damage and affect pubertal development and growth. The treatment of JIIM may also affect growth and pubertal maturity. The purpose of this review is to assess the effects of JIIM on growth and pubertal development. METHODS: A systematic review was conducted by searching Embase, MEDLINE, PubMed, PsycInfo, Cochrane, and Web of Science to identify studies published in English from inception to December 2024. Data were extracted regarding puberty- and development-related outcomes. Metaanalyses were conducted for outcomes measured consistently across studies, using the R package metafor (version 3.4-0). RESULTS: Of 5838 identified unique records, 24 were included. Interrater reliability for abstract and full-text screening was κ = 0.93 and κ = 1.0, respectively. Eighteen articles discussed growth (14/18 demonstrated decreased height or growth failure). Metaanalysis of 7 studies noted the overall prevalence of growth failure is 17.90% (95% CI 10.74-25.06). Five articles reported delays of secondary sex characteristics or puberty. Five articles discussed age of menarche onset; 4 reported delays and 1 did not. CONCLUSION: JIIM can cause several deleterious effects on development including growth failure. The effect of hormonal changes or delayed puberty has been less well studied. Endocrine abnormalities should be actively screened for and treated. Additional research is needed to assess long-term effects.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".