Serum autoantibodies that bind citrullinated fibrinogen are frequently found in patients with rheumatoid arthritis.
Bibliographic record
Abstract
OBJECTIVE: Autoantibodies that bind citrullinated antigens are a sensitive and specific marker for rheumatoid arthritis (RA). While synthetic cyclic citrullinated peptides (CCP) are typically used to identify these antibodies, little is known about antibody reactivity to the predominant citrullinated protein found in the inflamed synovium, citrullinated fibrinogen (CitFib). We assessed the prevalence of anti-CitFib antibodies in patients with various rheumatic diseases. METHODS: In total, 65 patients with established RA and 63 patients with other rheumatic diseases were tested for serum IgM rheumatoid factor (RF), IgG anti-CCP2, and IgG anti-CitFib antibodies. This cohort was used to determine optimal positive cutoff values for antibody reactivity to CitFib through receiver operating characteristic curve analysis. The specificity of these assays was confirmed with sera from 49 patients with psoriatic arthritis. RESULTS: Antibodies to both citrullinated antigens were identified in the majority of RA patients tested. The overall sensitivity and specificity of the assays were: CCP 82%, 96%, CitFib 75%, 98%, and IgM RF 80%, 64%, respectively. All but one patient that was positive for CitFib was also positive for CCP2, and close to half the RF-negative RA patients were positive for CitFib and CCP2. CONCLUSION: These results suggest that autoimmunity to CitFib is common in patients with RA and may play a role in disease pathogenesis.
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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 | 0.002 |
| 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) | 0.002 | 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".