Increased Cardiovascular Risk and All-cause Death in Patients with Behçet Disease: A Korean Nationwide Population-based Dynamic Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Behçet disease (BD) is a chronic inflammatory multiorgan disease. An increased risk of cardiovascular disease (CVD) and heightened death rate with BD have been suggested, but to our knowledge, a nationwide large-scale study has not been conducted to date. This study aimed to determine the overall CV risk and death rate in patients with BD versus controls using the Korean National Health Insurance Service claim database. METHODS: Patients with BD (n = 5576) with no previous history of CVD were selected from 2010 to 2014. An age- and sex-matched control population of individuals without BD (n = 27,880) was randomly sampled at a ratio of 5:1. Both cohorts were followed for incident CVD or all-cause death until 2015. RESULTS: The risks of myocardial infarction (HR 1.72, 95% CI 1.01-2.73) and stroke (HR 1.65, 95% CI 1.09-2.50) were significantly higher in patients with BD than in controls. Patients with BD also had a significantly higher risk of all-cause death (HR 1.82, 95% CI 1.40-2.37) compared to controls. CONCLUSION: Korean patients with BD had a higher overall risk of CVD than did those without BD. Therefore, patients with BD must be carefully monitored for the potential development of CVD to ensure that appropriate early treatments are delivered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".