Anti-neutrophil cytoplasmic antibodies in rheumatoid arthritis: two case reports and review of literature
Bibliographic record
Abstract
BACKGROUND: Anti-neutrophil cytoplasmic antibodies are typically detected in anti-neutrophil cytoplasmic antibody associated vasculitis, but are also present in a number of chronic inflammatory non-vasculitic conditions like rheumatoid arthritis. Rare cases of granulomatosis with polyangiitis (formerly known as Wegener's granulomatosis, a vasculitic disorder frequently associated with the presence of anti-neutrophil cytoplasmic antibodies) in patients with rheumatoid arthritis have been described in literature. CASE PRESENTATION: We report two middle-aged female patients with rheumatoid arthritis who developed anti-neutrophil cytoplasmic antibodies and symptoms reminiscent of granulomatosis with polyangiitis. Despite the lack of antibodies specific for proteinase 3 and the absence of a classical histology, we report a probable case of granulomatosis with polyangiitis in the first patient, and consider rheumatoid vasculitis in the second patient. CONCLUSION: Taken together with previous reports, these cases highlight that anti-neutrophil cytoplasmic antibodies have to be evaluated very carefully in patients with rheumatoid arthritis. In this context, anti-neutrophil cytoplasmic antibodies detected by indirect immunofluorescence appear to have a low diagnostic value for granulomatosis with polyangiitis. Instead they may have prognostic value for assessing the course of rheumatoid arthritis.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".