Is Statin Exposure Associated with Occurrence or Better Outcome in Giant Cell Arteritis? Results from a French Population-based Study
Bibliographic record
Abstract
OBJECTIVE: To investigate the potential association between statin use and giant cell arteritis (GCA) course. METHODS: Using the French National Health Insurance system, we included patients with incident GCA from the Midi-Pyrenees region, southern France, from January 2005 to December 2008 and randomly selected 6 controls matched by age, sex, and date of diagnosis. Statin exposure was compared between patients with GCA and their controls before GCA occurrence with a logistic regression. Influence of statin exposure on prednisone requirements during GCA course was explored with a Cox model, considering statin exposure as a time-varying variable. RESULTS: The cohort included 103 patients (80 women, mean age 74.8 ± 9 yrs, mean followup 48.9 ± 14.8 mos), compared to 606 controls. Statin exposure (27.2% of patients with GCA and 23.4% of controls) was not associated with GCA occurrence (adjusted OR 1.2, 95% CI 0.76-1.96; p = 0.41). Diabetes mellitus was significantly associated to GCA occurrence (adjusted OR 0.38, 95% CI 0.11-0.72; p = 0.008). After diagnosis, exposure to statins up to 20 months was associated with maintenance while taking low prednisone doses (p = 0.01). CONCLUSION: Statin exposure was not associated with GCA occurrence in the general population. However, exposure to statins up to 20 months may favor a quicker corticosteroid tapering. Based on those results, statin effect on GCA course should not be definitively ruled out.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".