O35. THE AUTOIMMUNE GENETIC ARCHITECTURE OF CHILDHOOD-ONSET RHEUMATOID ARTHRITIS
Bibliographic record
Abstract
Background: Genome-wide association studies have successfully identified susceptibility loci for many autoimmune diseases, including rheumatoid arthritis (RA) and juvenile idiopathic arthritis (JIA). About 5% of children with JIA have rheumatoid factor positive polyarthritis that is phenotypically similar to adults with seropositive RA, suggesting an extremely early onset of RA consistent with the childhood onset of RA (CORA). To better understand the genetic architecture of CORA risk relative to adult RA and other autoimmune diseases, we genotyped a cohort of CORA cases and controls on the Immunochip, a custom genotyping array. Methods: Immunochip genotyping was completed on 340 CORA cases and 11,624 controls. Following stringent SNP and sample QC, SNPs were tested for association with CORA using a logistic regression model with Caucasian admixture proportions (ADMIXTURE) as covariates (SNPLash). False discovery rate (FDR) adjusted p-values (PFDR) are reported to account for the actual number of tests computed. Weighted genetic risk scores (wGRS) of top RA loci and JIA oligoarthritis and RF negative polyarthritis risk loci were calculated and logistic regression performed to assess whether the scores were good predictors of CORA. Receiver operator characteristic (ROC) curves were generated to define the sensitivity and specificity of each wGRS and the area under the curve (AUC) was calculated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".