Increased Incidence of Giant Cell Arteritis in Urban Areas?
Bibliographic record
Abstract
Giant cell arteritis (GCA) is the most common systemic vasculitis in adults. The pathogenesis and the etiology of the disease are not fully understood, and environmental factors, which may influence the incidence and prevalence, are poorly investigated. Only a few small studies have previously addressed the potential influence of rural or urban residence on the occurrence of GCA1,2,3. In 2017 we published the results of a 41-year study of 743 patients with GCA from Bergen Health Area (Norway), in which incidence estimates were stratified by sex, age, biopsy result, and erythrocyte sedimentation rate4. Bergen Health Area is a mixed urban and rural area. In this report we present incidence estimates stratified by centrality, which may clarify the influence of rural versus urban residence on the incidence of GCA. This study was approved by REK sør-øst B regional ethics committee (study reference 2012/643/REK sør-øst B), who granted permission to access records without obtaining consent from patients owing to the long duration of the study and late onset of the disease. We performed a hospital-based … Address correspondence to Dr. L.K. Brekke, HSR AS, PB 2175, 5504 Haugesund, Norway. E-mail: lene.kristin.brekke{at}hsr.as
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".