Prevalence of rheumatic manifestations and antineutrophil cytoplasmic antibodies in haematological malignancies. A prospective study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the prevalence of antineutrophil cytoplasmic antibodies (ANCA) and rheumatic manifestations associated with chronic haematological malignancies. METHODS: Two groups of patients were prospectively studied (group I: 60 patients with myelodysplastic syndromes and group II: 140 patients with lymphoid malignancies) for clinical 'immune' manifestations and ANCA. RESULTS: In the myelodysplastic group, six patients had ANCA-negative systemic medium-size vasculitis, one had systemic vasculitis with cytoplasmic ANCA, one relapsing polychondritis, one giant cell arteritis, one polymyalgia rheumatica, one polyarthritis and two fasciitis. In group II, two patients had ANCA-negative systemic vasculitis, two had leucocytoclastic vasculitis associated with tuberculosis, two had polyarthritis, one polymyalgia rheumatica and one giant cell arteritis. Six sera were ANCA-positive with perinuclear pattern in four cases, atypical pattern in one and cytoplasmic pattern in one. Two sera had anti-myeloperoxidase (MPO) specificity, and others had no known specificity; none had anti-proteinase 3 (PR3) specificity. Global prevalence of ANCA in our cohort was 3%, similar to the French general population. CONCLUSION: Polyarteritis nodosa-type systemic vasculitis and polymyalgia rheumatica were the most frequent findings (18%) in myelodysplastic syndromes and particularly in chronic myelomonocytic leukaemia. ANCA were not helpful for the diagnosis of vasculitis. Vasculitis associated with infection, in particular tuberculosis, must be ruled out.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".