Autoantibodies to Dense Fine Speckles in Pediatric Diseases and Controls
Bibliographic record
Abstract
OBJECTIVE: Autoantibodies to the dense fine speckled 70 kDa antigen (DFS70) are reported to be more common in individuals who do not have an antinuclear antibody (ANA)-associated rheumatic disease (AARD) than in patients with AARD. The frequency of anti-DFS70 antibodies has been thoroughly studied in adult but not in pediatric populations. The primary objective of this observational study was to determine the frequency of anti-DFS70 in pediatric AARD and reference cohorts. METHODS: Sera from 743 children with AARD and related conditions, and 345 samples from reference cohorts (healthy children and those being investigated for AARD) were studied for anti-DFS70 autoantibodies as measured by a chemiluminescence immunoassay. A de-identified administrative database was used to retrieve demographic, serologic, and clinical data. RESULTS: Anti-DFS70 antibodies were seen in 2.1% of healthy children and in 4.5% of sera from pediatric individuals referred for ANA testing. The frequency of anti-DFS70 was highest in juvenile localized scleroderma (LS; 4/29, 13.8%), juvenile dermatomyositis (JDM; 2/11, 18.2%), childhood systemic lupus erythematosus (cSLE; 19/331, 5.7%), diffuse cutaneous systemic sclerosis (1/22, 4.5%), celiac disease (2/49, 4.1%), and juvenile idiopathic arthritis (JIA; 5/202, 2.5%). Of note, anti-DFS70 antibodies were observed in 3/26 children (11.5%) with uveitis and JIA-associated uveitis. CONCLUSION: The frequency of anti-DFS70 autoantibodies in healthy pediatric subjects is within the lower range of that reported in adults. Anti-DFS70 antibodies can be found in childhood SSc and cSLE, but has a remarkably high frequency in children with LS, JDM, and 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".