Development of Canadian Recommendations for the Management of ANCA-Associated Vasculitides: Results of the National Needs Assessment Questionnaire
Bibliographic record
Abstract
OBJECTIVES: To study variations in Canadian clinical practice patterns for the management of ANCA-associated vasculitis (AAV) and identify points to consider for the development of national recommendations. MATERIAL AND METHODOLOGY: A 30-item needs assessment questionnaire was sent to all members of the Canadian Vasculitis network (CanVasc), Canadian Rheumatology Association (CRA), Canadian Thoracic Society (CTS) and Canadian Society of Nephrology (CSN). Respondent characteristics, practice patterns, concerns and expectations were analyzed. RESULTS: Among 132 physicians who followed at least 1 vasculitis patient and responded to the survey, 39% stated that they felt confident in their management of AAV. Several variations in practice were observed regarding diagnostic procedure, induction and maintenance treatments and use of biologics; some were due to logistic constraints (difficulties in access to some specific tests, drugs or care; lack of health care coverage for the costs). The top 5 topics for which recommendations are expected involve treatment for remission induction, maintenance, refractory disease, and relapse as well as biologics. CONCLUSION: Practice variations identified in this needs assessment survey will serve to formulate key questions for the development of CanVasc recommendations.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".