Causes of Cardiovascular Ischemic Events in Giant Cell Arteritis
Bibliographic record
Abstract
Giant cell arteritis (GCA) is a granulomatous vasculitis with rising incidence in the sixth to eighth decade of age, when cardiovascular (CV) events put survival, health, and function per se at risk1. The outcomes of GCA patients with marked tissue tropism in certain vascular regions might be strongly influenced by CV events2. CV disease seems to be pronounced in patients with GCA in the first month after diagnosis compared to the age-matched general population, but the risk may also be increased in the followup period (median 3.9 yrs)3. CV events including strokes in patients with GCA, especially in the longterm followup, are not well explored. It remains difficult to distinguish whether the events are related to the inflammatory disease or to nonspecific atherosclerotic lesions, other arterial wall changes due to vascular aging, or other accompanying CV risk factors3,4. In the current issue of The Journal , Pugnet, et al describe predictors for CV hospitalization of patients with GCA in France based on an administrative database, focusing on the effect of statin exposure5. The authors conclude that patients with GCA have a higher risk for a “new” CV disease leading to an inpatient stay in a cardiology unit, stroke … Address correspondence to Dr. J.G. Richter, Policlinic for Rheumatology and Hiller Research Centre for Rheumatology, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, 40225 Düsseldorf, Germany. E-mail: richter{at}rheumanet.org
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".