Association between infection and the onset of giant cell arteritis and polymyalgia rheumatica: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: We aimed to analyse the association between infections and the subsequent risk of giant cell arteritis (GCA) and/or polymyalgia rheumatica (PMR) by a systematic review and a meta-analysis of observational studies. METHODS: Two databases (Medline and Embase) were systematically reviewed. Epidemiological studies studying the association between any prior infection and the onset of GCA/PMR were eligible. Risk of bias was assessed using the Newcastle-Ottawa quality assessment scale. Outcomes and pooled statistics were reported as OR and their 95% CI. RESULTS: (HZ) infections with pooled OR (95% CI) of 1.27 (1.18 to 1.37) and 1.20 (1.08 to 1.21), respectively. When analysed separately, hospital-treated and community-treated infections, were still significantly associated with the risk of GCA, but only when infections occurring within the year prior to diagnosis were considered (pooled OR (95% CI) 1.92 (1.67 to 2.21); 1.67 (1.54 to 1.82), respectively). This association was no longer found when infections occurring within the year prior to diagnosis were excluded. CONCLUSION: Our study showed a positive association between the risk of GCA and prior overall infections (occurring in the year before), and prior HZ infections. Infections might be the reflect of an altered immunity of GCA patients or trigger the disease. However, reverse causation cannot be excluded.CRD42023404089.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".