Which Patients with Giant Cell Arteritis Will Develop Cardiovascular or Cerebrovascular Disease? A Clinical Practice Research Datalink Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the risk of cerebrovascular disease and cardiovascular disease (CVD) in patients with giant cell arteritis (GCA), and to identify predictors. METHODS: The UK Clinical Practice Research Datalink 1991-2010 was used for a parallel cohort study of 5827 patients with GCA and 37,090 age-, sex-, and location-matched controls. A multivariable competing risk model (non-cerebrovascular/CV-related death as the competing risk) determined the relative risk [subhazard ratio (SHR)] between patients with GCA compared with background controls for cerebrovascular disease, CVD, or either. Each cohort (GCA and controls) was then analyzed individually using the same multivariable model, with age and sex now present, to identify predictors of CVD or cerebrovascular disease. RESULTS: Patients with GCA, compared with controls, had an increased risk SHR (95% CI) of cerebrovascular disease (1.45, 1.31-1.60), CVD (1.49, 1.37-1.62), or either (1.47, 1.37-1.57). In the GCA cohort, predictors of "cerebrovascular disease or CVD" included increasing age, > 80 years versus < 65 years (1.98, 1.62-2.42), male sex (1.20, 1.05-1.38), and socioeconomic status, most deprived quintile versus least deprived (1.34, 1.01-1.78). These predictors were also present within the non-GCA cohort. CONCLUSION: Patients with GCA are more likely to develop cerebrovascular disease or CVD than age-, sex-, and location-matched controls. In common with the non-GCA cohort, patients who are older, male, and from the most deprived compared with least deprived areas have a higher risk of cerebrovascular disease or CVD. Further work is needed to understand how this risk may be mediated by specific behavioral, social, and economic factors.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".