Rheumatoid Arthritis-associated Polymorphisms at 6q23 Are Associated with Radiological Damage in Autoantibody-positive RA
Bibliographic record
Abstract
OBJECTIVE: Recent studies have identified 6q23 as an important susceptibility locus for rheumatoid arthritis (RA), with risk alleles at 3 single-nucleotide polymorphisms combining to give an effect size greater than that of these markers individually. We investigated whether these polymorphisms are also associated with disease severity measured by radiological damage. METHODS: We studied 927 patients from a cross-sectional RA cohort. Median Larsen scores (LS) read from radiographs taken at study entry were compared by genotype at rs6920220, rs13207033, and rs5029937 according to a dominant model using negative binomial regression with stratification for autoantibody status. RESULTS: Median LS was associated with genotype at rs6920220 [LS 31 GG vs 36 GA/AA (p=0.02) in cyclic citrullinated peptide+ (CCP) RA] and rs13020220 [LS 37 GG vs 29 GA/AA (p=0.02) in CCP+ RA] only in autoantibody-positive RA, with no association at rs5029937. Association was stronger for these markers in combination [LS 28 vs 42 for lowest vs highest risk genotype combination in rheumatoid factor positivity (p=0.007), LS 28 vs 37 for anti-CCP+ (p=0.01)]. CONCLUSION: Established RA risk markers at 6q23 are associated also with radiographic severity in autoantibody-positive RA; as for susceptibility, the association for these markers in combination is stronger than that for markers alone.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".