Infections Are Associated With Increased Risk of Giant Cell Arteritis: A Population-based Case-control Study from Southern Sweden
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between infections and the subsequent development of giant cell arteritis (GCA) in a large population-based cohort from a defined geographic area in Sweden. METHODS: Patients diagnosed with biopsy-confirmed GCA between 2000 and 2016 were identified through the database of the Department of Pathology in Skåne, the southernmost region of Sweden. For each GCA case, 10 controls matched for age, sex, and area of residence were randomly selected from the general population. Using the Skåne Healthcare Register, we identified all infection events prior to patients' date of GCA diagnosis and controls' index date. With infection as exposure, a conditional logistic regression model was employed to estimate the OR for developing GCA. The types of infections contracted nearest in time to the GCA diagnosis/index date were identified. RESULTS: A total of 1005 patients with biopsy-confirmed GCA (71% female) and 10,050 controls were included in the analysis. Infections were more common among patients subsequently diagnosed with GCA compared to controls (51% vs 41%, OR 1.78, 95% CI 1.53-2.07). Acute upper respiratory tract infection (OR 1.77, 95% CI 1.47-2.14), influenza and pneumonia (OR 1.72, 95 % CI 1.35-2.19), and unspecified infections (OR 5.35, 95 % CI 3.46-8.28) were associated with GCA. Neither skin nor gastrointestinal infections showed a correlation. CONCLUSION: Infections, especially those of the respiratory tract, were associated with subsequent development of biopsy-confirmed GCA. Our findings support the hypothesis that a range of infections may trigger 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".