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 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".