Practice Patterns of Induction Therapy in Severe ANCA-Associated Vasculitis: An International Physician Survey
Bibliographic record
Abstract
Background: Therapies for ANCA-associated vasculitis (AAV) have evolved over the last 3 decades. In light of new data on plasma exchange (PLEX) and glucocorticoid (GC) use, as well as recent approval of avacopan (AVP), various medical societies have updated their AAV management guidelines. Here, we explored practice patterns of induction therapy in severe AAV and ascertained differences in management by physician specialty, practice setting, and volume of AAV patients. Methods: A 65-item anonymous research survey addressing physician/practice characteristics, AAV induction therapy approaches, prophylactic measures, and laboratory monitoring was sent to physicians by e-mail and social media platforms after IRB approval. Practice patterns within the last 5 years were examined based on physician specialty, practice setting, and volume of AAV patients. Descriptive statistics, chi-square, t-test, and Fisher’s exact were used as appropriate. Results: There were 308 responses (52% nephrologists, 41% rheumatologists). Of all participants, 29% practiced in the United States, 20% in India, 9% in the United Kingdom, 6% in Canada, and the remainder in other countries. Pulse methylprednisolone (MeP) was used by 94%, reduced dose GC by 70%, PLEX by 38%, rituximab (RTX) by 92%, cyclophosphamide (CYC) by 89%, and AVP by 12%. There were significant differences in use of reduced dose GC, PLEX, and RTX brand by physician specialty and by AAV patient volume (Table). Significant differences were seen in regards to pulse MeP dose (p<0.001), treatment of severe AAV presentations (p<0.001), CYC route and duration (p=0.005, p<0.001), and PJP prophylaxis (p<0.005) by physician specialty, RTX and AVP use by volume of AAV patients (p=0.003, 0.001), and CYC use by practice setting (p<0.001). Conclusions: Our survey highlights significant differences in AAV induction therapy practices based on specialty, practice setting, and AAV patient volume. Additionally, one third of physicians continue to use standard GC. Funding: Clinical Revenue Support
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".