Clinician views concerning the prevalence and impact of granulomas on the diagnosis, management, and outcomes of ANCA-associated vasculitis
Bibliographic record
Abstract
OBJECTIVES: It is unclear whether clinicians agree which manifestations of ANCA-associated vasculitides (AAV) is associated with necrotizing granulomas or if their presence affects clinical decision-making. METHODS: We surveyed physicians experienced in caring for individuals with AAV, querying: experience with AAV; beliefs concerning how granulomas affect the diagnosis, treatments and outcomes of AAV; beliefs concerning the frequency with which granulomas are found in 36 manifestations of AAV; and degree to which granulomas change choice of induction therapy for specific manifestations of AAV. We analysed responses using descriptive statistics and multivariable linear regression. RESULTS: We received 142 responses from 35 countries. Responses had a median Likert response ≥5 on a seven-point scale (equal to 'partially agree') that granulomatous manifestations respond differently to therapy, increase risk of relapse and increase organ damage. Four of 36 manifestations were believed to be caused by granulomas in a median of ≥75% of cases (on a scale of 0 = never to 100 = always caused by granuloma), 19 in a median of ≤25% of cases, and 13 in intermediate medians. The perceived degree to which granulomas caused manifestations was not associated with changes in therapy to induce remission in severe AAV (P-values 0.26-0.93 across scenarios). CONCLUSIONS: Physicians experienced in vasculitis generally agree on which manifestations of AAV are and are not caused by granulomas and that granulomatous inflammation alters the natural history and treatment of AAV. However, the presence of granulomatous manifestations did not alter treatment choices to induce remission in severe 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 | 0.008 | 0.041 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".