Myeloperoxidase-antineutrophil Cytoplasmic Antibodies (MPO-ANCA) and Proteinase 3-ANCA without Immunofluorescent ANCA Found by Routine Clinical Testing
Bibliographic record
Abstract
OBJECTIVE: Concurrent testing for serum antineutrophil cytoplasmic antibodies (ANCA) by indirect immunofluorescence (IF) and by antiproteinase 3 (PR3)/antimyeloperoxidase (MPO) antibody assays may identify patients with PR3-ANCA or MPO-ANCA despite a negative IF (IF-negative MPO/PR3-positive); however, the significance of this result is not clear. We sought to determine whether IF-negative, MPO/PR3-positive results identified any cases of clinically meaningful systemic vasculitis at our institution. METHODS: We conducted a retrospective chart review of all IF-negative, MPO/PR3-positive patients identified at our institution over a 2-year period. RESULTS: Of the 2345 samples tested over 2 years, 1998 were IF-negative. Among these IF-negative samples, 49 samples (2.5%) derived from 38 patients tested positive for MPO-ANCA or PR3-ANCA. Only 1 IF-negative, MPO/PR3-positive patient was subsequently diagnosed with ANCA-associated vasculitis (AAV). Eleven IF-negative, MPO/PR3-positive patients (29%) had been previously diagnosed and treated for AAV, all with positive IF and antibody tests prior to treatment. Four patients had evidence of cutaneous vasculitis not attributed to AAV, while several of the remaining IF-negative, MPO/PR3-positive patients had other immunologic disorders, including systemic lupus erythematosus (5 patients) and inflammatory bowel disease (3 patients). CONCLUSION: In this real-life cohort assayed simultaneously by IF and multiplexed bead assays, the detection of MPO-ANCA or PR3-ANCA without a positive IF rarely led to a new diagnosis of systemic vasculitis, and was more likely to occur in the context of a non-vasculitic inflammatory condition. Our results suggest that concurrent IF and MPO/PR3 testing may be of limited use in preventing a missed diagnosis of new-onset AAV.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".