The Southern Alberta Vasculitis Registry: Review of the Current State and Future Initiatives
Bibliographic record
Abstract
Objectives Established in 2012, the Southern Alberta Vasculitis Registry (SAVR) was intended to create a reliable, local database for vasculitis research. In this quality improvement study, we review the current state of the SAVR and propose avenues for future use and improvement. Methods The SAVR collected patient demographics and disease-related information from patients referred to the Southern Alberta Vasculitis Clinic. De-identified patient sera samples were also collected and stored by Mitogen labs. The SAVR findings up to August 2024 were reviewed. Descriptive statistics were used to describe the findings. A search of the literature was also conducted to determine if there were any publications produced relating to the SAVR. Results 343 patients were identified in total. 287 patients were living, and 56 patients were deceased. The average age of patients in the registry was 58.1 (median 60, range 20-95). 33% (113/343) patients were male, and 67% (210/343) patients were female (Figure). Among the living patients, 9.8% (28/287) had active vasculitis disease, 76% (219/287) were in documented remission. The average duration of vasculitis for registry patients was 10.3 years (median 10 years, range 1-43). There were 82 cases of granulomatosis with polyangiitis, 45 cases of giant cell arteritis, 31 cases of Takayasu, 26 cases of Behcet’s, 23 cases of secondary vasculitis (most common cause being rheumatoid arthritis representing 22% of these cases), 21 cases of IgA vasculitis, 20 cases of microscopic polyangiitis, 19 cases of single organ vasculitis, 18 cases of eosinophilic granulomatosis with polyangiitis, 12 cases of polyarteritis nodosa, 12 cases of other small vessel vasculitis, 9 cases of other large vessel vasculitis, and 23 cases of unknown/unspecified vasculitis. To date, 3 papers have been published using data from the SAVR: A three-year review and 2 biomarker studies (LAMP-2 and anti-DSF70).[1,2] Conclusion Given the low incidence and prevalence of vasculitis, the presence of a vasculitis database is of considerable importance. The current registry holds valuable sociodemographic data and disease information (ex. vasculitis disease type, activity, treatment). In addition to future biomarker studies, this registry will serve as a resource for epidemiological research (ex. incidence, morbidity/mortality studies), quality of life studies, and potential clinical trials. Reviewing the literature, we found that high-yield initiatives for knowledge translation would include implementing data collection methods which would harmonize with other registries to allow research collaboration and the application of registry findings to the creation of local clinical pathways.[3] [1.] Moran-Toro C. J Rheumatol Res 2020;2(1):70-4. [2.] Laestadius A. [abstract]. Arthritis Rheumatol 2020;72(Suppl 4). [3.] Gisslander K. Ann Rheum Dis 2023;83(1):112-20.
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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 | 0.057 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.025 | 0.037 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".