Rate of Comorbidities in Giant Cell Arteritis: A Population-based Study
Bibliographic record
Abstract
OBJECTIVE: To compare the rate of occurrence of comorbidities, including severe infections, in a population-based cohort of patients with biopsy-proven giant cell arteritis (GCA) with a reference population in Southern Sweden. METHODS: The study included a population-based cohort of biopsy-proven GCA cases diagnosed between 1998 and 2010 from the Skåne region in Southern Sweden (population: 1.2 million). For each patient, 4 reference subjects were identified from the general population and matched for age, sex, area of residence, and date of diagnosis of GCA. Using the Skåne Healthcare Register, comorbidities and severe infections (requiring hospitalization) diagnosed after GCA onset were identified. The rate of the first occurrence of each comorbidity was the result of dividing the number of subjects with a given comorbidity by the person-years of followup. The rate ratio (RR; GCA:reference population) was also calculated. RESULTS: There were 768 patients (571 women) with GCA and 3066 reference persons included in the study. The RR were significantly elevated for osteoporosis (2.81, 95% CI 2.33-3.37), followed by venous thromboembolic diseases (2.36, 95% CI 1.61-3.40), severe infections (1.85, 95% CI 1.57-2.18), thyroid diseases (1.55, 95% CI 1.25-1.91), cerebrovascular accidents (1.40, 95% CI 1.12-1.74), and diabetes mellitus (1.29, 95% CI 1.05-1.56). The RR for ischemic heart disease was elevated, but did not reach statistical significance (1.20, 95% CI 1.00-1.44). CONCLUSION: Patients with GCA have higher rates of selected comorbidities, including severe infections, compared with a reference population. Several of these comorbidities may be related to treatment with glucocorticosteroids, emphasizing the unmet need to find alternative treatments for GCA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".