Incidence of granulomatosis with polyangiitis (Wegener's) in Greenland and the Faroe Islands: epidemiology of an ANCA-associated vasculitic syndrome in two ethnically distinct populations in the North Atlantic area.
Bibliographic record
Abstract
OBJECTIVES: Previous studies suggest that the incidence of granulomatosis with polyangiitis (Wegener's; GPA) increases along a south-north gradient in the Northern Hemisphere with an incidence of 8.0/million/year reported for the population of Northern Norway. In the present study, we assessed the incidence of GPA in the predominantly Inuit population of Greenland and in the Caucasian population of the Faroe Islands. METHODS: Greenlandic and Faroese patients affected by severe rheumatic diseases are routinely referred to the National University Hospital in Denmark for treatment. By means of the Danish National Hospital register, we identified all Greenlandic and Faroese patients treated at the hospital under a diagnosis of GPA during 1992-2011. For each patient, the GPA diagnosis was validated by medical files review. RESULTS: One patient born and living in Greenland and 6 from the Faroe Islands were identified. The incidence of GPA was 1.0/million/year (95% CI 0.02-5.6) in Greenland and 6.4/million/year (95% 2.4-14.0) in the Faroe Islands. During the period of study, no cases of GPA occurred among Greenlanders aged 0-44 years, while an incidence of 4.1/million/year (95% CI: 0.1-22.9) was calculated for those aged ≥45 years. In the Faroese population, incidences of 1.7/million/year (95% CI 0.4-9.4) and 14.8/million/year (95% CI 4.8-34.6) were calculated for the age-groups 0-44 and ≥45 years, respectively. CONCLUSIONS: The occurrence of GPA is lower among Inuit in Greenland than among Caucasians living in the Faroe Islands. This observation demonstrates that the risk of GPA varies across ethnic groups populating the northernmost regions of the world.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".