Does geographic location affect incidence of ANCA-associated renal vasculitis in northern Saskatchewan, Canada?
Bibliographic record
Abstract
OBJECTIVES: To compare the incidence of renal ANCA-associated vasculitis (AAV) in urban vs more rural populations in northern Saskatchewan, and as a secondary objective to compare time to diagnosis between these geographic areas. METHODS: Northern Saskatchewan has a population of 562,882 of which approximately 260 600 live in the major urban area. A pathology database search for renal biopsy reports suggestive of AAV between January 2007 and December 2011 and subsequent chart review yielded 33 new diagnoses of granulomatosis polyangiitis (GPA) or microscopic polyangiitis (MPA). Data extraction included demographics, residential data, serological status, recorded symptom onset and estimations of BVAS, five-factor score (FFS) and vasculitis damage index (VDI). RESULTS: Of 33 renal AAV cases, 24.2 % (n = 8) lived within the city. The incidence rate for urban residents was 6.1 cases/million/year, and for those residing elsewhere, 16.5 cases/million/year. The odds ratio for the more rural population was 2.69 (95% CI 1.3, 7.5). Mean time to diagnosis was 1.33 (s.d. 0.94) months for urban and 3.52 (s.d. 3.83) months for more rural patients (P = 0.002, 95% CI 0.7, 3.9). Secondary analysis supported these observations with higher BVAS, VDI and FFS scores in patients living outside the urban centre. CONCLUSION: The incidence of renal biopsy-proven AAV was higher in patients living in northern Saskatchewan smaller communities and rural settings. A significantly longer time to diagnosis existed for patients outside the urban centre and was associated with poorer BVAS, VDI and FFS scores.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".