CanVasc Consensus Recommendations for the Management of Antineutrophil Cytoplasm Antibody-associated Vasculitis: 2020 Update
Bibliographic record
Abstract
OBJECTIVE: In 2015, the Canadian Vasculitis Research Network (CanVasc) created recommendations for the management of antineutrophil cytoplasm antibody (ANCA)-associated vasculitides (AAV) in Canada. The current update aims to revise existing recommendations and create additional recommendations, as needed, based on a review of new available evidence. METHODS: A needs assessment survey of CanVasc members informed questions for an updated systematic literature review (publications spanning May 2014 to September 2019) using Medline, Embase, and Cochrane. New and revised recommendations were developed and categorized according to the level of evidence and strength of each recommendation. The CanVasc working group used a 2-step modified Delphi procedure to reach > 80% consensus on the inclusion, wording, and grading of each new and revised recommendation. RESULTS: Eleven new and 16 revised recommendations were created and 12 original (2015) recommendations were retained. New and revised recommendations are discussed in detail within this document. Five original recommendations were removed, of which 4 were incorporated into the explanatory text. The supplementary material for practical use was revised to reflect the updated recommendations. CONCLUSION: The 2020 updated recommendations provide rheumatologists, nephrologists, and other specialists caring for patients with AAV in Canada with new management guidance, based on current evidence and consensus from Canadian experts.
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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 | 0.040 | 0.106 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.021 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".