Predictors of Cardiovascular Hospitalization in Giant Cell Arteritis: Effect of Statin Exposure. A French Population-based Study
Bibliographic record
Abstract
OBJECTIVE: To identify predictors and protectors for cardiovascular hospitalization in a giant cell arteritis (GCA) population-based cohort. 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 sex and age at calendar date. We used a Cox model to identify independent predictors for cardiovascular hospitalization [combining stroke, coronary artery disease (CAD), heart failure, peripheral arterial disease, or cardiac arrhythmias]. RESULTS: Among 103 patients with GCA followed 48.9 ± 14.8 months, the incidence rates of hospitalization for cardiovascular disease, atherosclerotic disease (combining stroke, CAD, and peripheral arterial disease), heart failure, and cardiac arrhythmias were 48.6, 17.5, 14.8, and 9.8 events per 1000 person-years versus 14.9, 4.6, 6.2, and 2.5 events per 1000 person-years among controls, respectively. In patients with GCA, cardiovascular comorbidities at diagnosis (HR 6.2, 2.0-19.2), age over 77 years (HR 5.0, 1.40-17.54), as well as the cumulative defined daily dose of statins (HR 0.993, 0.986-0.999) were independent predictors for subsequent cardiovascular hospitalization. None of the 25 patients with GCA who were taking platelet aggregation inhibitors experienced a cardiovascular hospitalization during followup. CONCLUSION: Patients with GCA present a high risk of cardiovascular hospitalization after diagnosis. In patients with incident GCA from the Midi-Pyrenees region, southern France, statin therapy was associated with reduced cardiovascular hospitalizations.
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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 | 0.000 |
| 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) | 0.001 | 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".