Subglottic stenosis and endobronchial disease in granulomatosis with polyangiitis
Bibliographic record
Abstract
OBJECTIVES: To describe tracheobronchial disease in patients with granulomatosis with polyangiitis (GPA) and evaluate the utility of dynamic expiratory CT to detect large-airway disease. METHODS: Demographic and clinical features associated with the presence of subglottic stenosis (SGS) or endobronchial involvement were assessed in a multicentre, observational cohort of patients with GPA. A subset of patients with GPA from a single-centre cohort underwent dynamic chest CT to evaluate the airways. RESULTS: Among 962 patients with GPA, SGS and endobronchial disease were identified in 95 (10%) and 59 (6%) patients, respectively. Patients with SGS were more likely to be female (72% vs 53%, P < 0.01), younger at time of diagnosis (36 vs 49 years, P < 0.01), and have saddle-nose deformities (28% vs 10%, P < 0.01), but were less likely to have renal involvement (39% vs 62%, P < 0.01). Patients with endobronchial disease were more likely to be PR3-ANCA positive (85% vs 66%, P < 0.01), with more ENT involvement (97% vs 77%, P < 0.01) and less renal involvement (42% vs 62%, P < 0.01). Disease activity in patients with large-airway disease was commonly isolated to the subglottis/upper airway (57%) or bronchi (32%). Seven of 23 patients screened by dynamic chest CT had large-airway pathology, including four patients with chronic, unexplained cough, discovered to have tracheobronchomalacia. CONCLUSION: SGS and endobronchial disease occur in 10% and 6% of patients with GPA, respectively, and may occur without disease activity in other organs. Dynamic expiratory chest CT is a potential non-invasive screening test for large-airway involvement in GPA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".