Cardiovascular Risk Prevention in a Canadian Population of Patients with Giant Cell Arteritis
Bibliographic record
Abstract
To the Editor: Giant cell arteritis (GCA) is a large-vessel vasculitis with a predilection for cranial arteries. Based on retrospective studies, GCA is associated with an increased prevalence of traditional cardiovascular (CV) risk factors and CV events, including myocardial infarction and stroke1,2,3,4,5. However, studies regarding management of CV disease (CVD) in GCA are lacking. We report here on CV risk factors and CV complications, and describe physician adherence to guidelines for prevention of CV events in a Canadian population with GCA. We studied a single-center retrospective cohort of patients with GCA assessed at the St. Joseph’s Health Care rheumatology clinic in London, Ontario, by rheumatologists between 2006 and 2015. Patients were identified by diagnostic codes, met American College of Rheumatology classification criteria for GCA, and had at least 1 followup visit. CV complication (CVC) was defined as a composite outcome: any acute coronary syndrome (ACS), stroke, transient ischemic event, or severe peripheral vascular disease (PVD) requiring surgical intervention. Ethics approval was given by … Address correspondence to Dr. L.J. Barra, St. Joseph’s Health Care, 268 Grosvenor St., Room D2-160, London, Ontario N6A 4V2, Canada. E-mail: lillian.barra{at}sjhc.london.on.ca
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".