Clinician Views and Clinic-Pathologic Correlations Concerning the Prevalence and Impact of Granulomas on Diagnosis, Management, and Outcomes of ANCA-Associated Vasculitis: An International Survey
Bibliographic record
Abstract
Objectives Granulomatous inflammation occurs in ANCA-associated vasculitis (AAV), however, which manifestations are attributable to granulomas are unclear. We sought to understand clinician beliefs concerning which manifestations of AAV are attributable to granulomas and if the presence of granulomas informs diagnostic, prognostic and treatment decisions. Methods We conducted an international survey of physicians who care for individuals with AAV with the following domains: clinical experience; the extent to which the presence of granulomatous manifestations impact diagnosis, prognosis, and treatment; how frequently granulomas are responsible for individual AAV manifestations; and how granulomatous manifestations affect choice of induction therapy. The association of manifestations on induction therapy was assessed using a multivariable linear regression model adjusted for clinician demographics and practice experience. Results We received 161 responses, of which 142 from 35 countries contained usable data. Respondents at least partially agreed (median response ≥5 on a 7-point Likert scale) that granulomatous manifestations increased risk of relapse, respond differently to therapy, and caused more damage than non-granulomatous manifestations. Across the 36 manifestations of AAV, respondents considered pulmonary nodules, retro-orbital masses, sinus involvement, and subglottic stenosis caused by granuloma (caused by granulomas in ≥70% of cases). Twenty-three were considered to not be caused by granulomas in most cases (≤30% of cases). Induction therapy did not differ on the basis of granulomatous manifestations (p-value range=0.26-0.97). Conclusion Respondents similarly identify that granuloma may be important in the management of patients with AAV and which manifestations are granulomatous, and which are non-granulomatous. Despite this, they did not differ in the choice of induction therapy.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".