Prevalence and titres of antinuclear antibodies in juvenile idiopathic arthritis: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Antinuclear antibodies (ANA) detected in juvenile idiopathic arthritis (JIA) sera are considered to be a biomarker for JIA-related uveitis. There is an unclear consensus on the screening dilutions of ANA as detected by the HEp-2 indirect immunofluorescence assay (IFA) that should be used when predicting the risk of uveitis in JIA. The primary aim of this systematic review and meta-analysis was to summarize the evidence regarding ANA prevalence and performance in JIA and JIA-associated uveitis. METHODS: A search of five databases identified 1766 abstracts, using the search terms juvenile idiopathic arthritis; pediatric; sensitivity or diagnostic; and ANA. Studies that met inclusion/exclusion criteria were analyzed for the proportion of JIA patients with a positive ANA. Forest plots and pooled estimates were generated for the proportion of JIA patients and those with uveitis who were positive for ANA stratified by screening dilution. Study heterogeneity was also assessed. RESULTS: Twenty-eight studies met inclusion criteria yielding 6250 unique patients; 5902 had JIA and 348 were healthy controls or were known to have other autoimmune diseases. The most reported IFA serum screening dilution was ≥1:80, representing 41.9% of patients and this screening dilution had the highest proportion of JIA ANA positivity (41.0%; 95% CI 25.0%-57.0%). ANA screening for JIA uveitis had a sensitivity and specificity of ANA at ≥1:40 of 75% (95% CI 46%-100%) and 66% (95% CI 39%-93%), respectively. There was significant study heterogeneity across both JIA subtypes and ANA titres. CONCLUSIONS: Although there was a large variation of ANA IFA screening dilutions used for investigation of JIA, the most common dilution was 1:80. The current literature has several important deficiencies that are identified in this review requiring additional studies to inform the ANA screening dilutions of clinical value in JIA and JIA-associated uveitis.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".