Anti-Fibrillarin Antibody in African American Patients with Systemic Sclerosis: Immunogenetics, Clinical Features, and Survival Analysis
Bibliographic record
Abstract
OBJECTIVE: Anti-U3-RNP, or anti-fibrillarin antibodies (AFA), are detected more frequently among African American (AA) patients with systemic sclerosis (SSc) compared to other ethnic groups and are associated with distinct clinical features. We examined the immunogenetic, clinical, and survival correlates of AFA in a large group of AA patients with SSc. METHODS: Overall, 278 AA patients with SSc and 328 unaffected AA controls were enrolled from 3 North American cohorts. Clinical features, autoantibody profile, and HLA class II genotyping were determined. To compare clinical manifestations, relevant clinical features were adjusted for disease duration. Cox proportional hazards regression was used to determine the effect of AFA on survival. RESULTS: Fifty (18.5%) AA patients had AFA. After Bonferroni correction, HLA-DRB1*08:04 was associated with AFA, compared to unaffected AA controls (OR 11.5, p < 0.0001) and AFA-negative SSc patients (OR 5.2, p = 0.0002). AFA-positive AA patients had younger age of disease onset, higher frequency of digital ulcers, diarrhea, pericarditis, higher Medsger perivascular and lower Medsger lung severity indices (p = 0.004, p = 0.014, p = 0.019, p = 0.092, p = 0.006, and p = 0.016, respectively). After adjustment for age at enrollment, AFA-positive patients did not have different survival compared to patients without AFA (p = 0.493). CONCLUSION: Our findings demonstrate strong association between AFA and HLA-DRB1*08:04 allele in AA patients with SSc. AA SSc patients with AFA had younger age of onset, higher frequency of digital ulcers, pericarditis and severe lower gastrointestinal involvement, but less severe lung involvement compared to AA patients without AFA. Presence of AFA did not change survival.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".