Pulmonary Involvement in Antineutrophil Cytoplasmic Antibodies (ANCA)-associated Vasculitis: The Influence of ANCA Subtype
Bibliographic record
Abstract
OBJECTIVE: To describe pulmonary involvement at time of diagnosis in antineutrophil cytoplasmic antibodies (ANCA)-associated vasculitis (AAV), as defined by computed tomography (CT). METHODS: Patients with thoracic CT performed on or after the onset of AAV (n = 140; 75 women; granulomatosis with polyangiitis, n = 79; microscopic polyangiitis MPA, n = 61) followed at a tertiary referral center vasculitis clinic were studied. Radiological patterns of pulmonary involvement were evaluated from the CT studies using a predefined protocol, and compared to proteinase 3 (PR3)-ANCA and myeloperoxidase (MPO)-ANCA specificity. RESULTS: Of the patients, 77% had an abnormal thoracic CT study. The most common abnormality was nodular disease (24%), of which the majority were peribronchial nodules, followed by bronchiectasis and pleural effusion (19%, each), pulmonary hemorrhage and lymph node enlargement (14%, each), emphysema (13%), and cavitating lesions (11%). Central airways disease and a nodular pattern of pulmonary involvement were more common in PR3-ANCA-positive patients (p < 0.05). Usual interstitial pneumonitis (UIP) and bronchiectasis were more prevalent in MPO-ANCA-positive patients (p < 0.05). Alveolar hemorrhage, pleural effusion, lymph node enlargement, and pulmonary venous congestion were more frequent in MPO-ANCA-positive patients. CONCLUSION: Pulmonary involvement is frequent and among 140 patients with AAV who underwent a thoracic CT study, almost 80% have pulmonary abnormalities on thoracic CT. Central airway disease occurs exclusively among patients with PR3-ANCA while UIP were mainly seen in those with MPO-ANCA. These findings may have important implications for the investigation, management, and pathogenesis of 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".