Hospitalized Infections in Giant Cell Arteritis — A Population-based Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To assess the occurrence of infections requiring or acquired during hospitalization in patients with giant cell arteritis (GCA). METHODS: We retrospectively reviewed a population-based incidence cohort of patients with GCA diagnosed between 1950 and 2009 and compared this cohort with a non-GCA one matched for age, sex, and calendar year from the same population. RESULTS: We identified 245 patients in the GCA cohort and 245 patients in the non-GCA cohort. Seventy-four GCA subjects (134 episodes) and 79 non-GCA (153 episodes) had infections requiring or acquired during hospitalization [rate ratio (RR) 0.94; 95% CI 0.74, 1.18]. Sixty-seven subjects (107 episodes) in the GCA cohort and 63 subjects (110 episodes) in non-GCA cohort required hospitalization secondary to an infection (RR 1.04; CI 0.80, 1.36). Pneumonia, urinary tract infections (UTI), skin and soft tissue infections accounted for the majority of infections requiring hospitalization and had similar occurrence in both cohorts. UTI accounted for the majority of infections requiring hospitalization in the first 6 months after GCA incidence (RR 3.93; CI 0.85, 56.52). No difference between the 2 cohorts was noted in overall infections acquired during hospitalization (RR 0.68; CI 0.41, 1.08). CONCLUSION: There is no overall increased risk of infections requiring or acquired during hospitalization in patients with GCA who are taking glucocorticoid therapy. There may be an increased risk of infections requiring hospitalization, especially of the urinary tract, in the first 6 months after GCA incidence, although this did not achieve statistical significance in our study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".