Cardiovascular disease (CVD) in Canadians with haemophilia: Age‐Related CVD in Haemophilia Epidemiological Research (ARCHER study)
Bibliographic record
Abstract
INTRODUCTION: Improvements in haemophilia care have increased life expectancy in persons with haemophilia (PWH). This ageing population presents clinicians with management challenges as they develop age-related comorbidities such as cardiovascular disease (CVD). AIMS: To assess the epidemiology of CVD risk factors and events in an ageing Canadian haemophilia population. METHODS: A retrospective, multicentre chart review was carried out at five Canadian Hemophilia Treatment Centres. PWH (A and B) ≥35 years old were included and data were extracted on CVD risk factors and events. RESULTS: Data from 294 patients' charts were analysed including 222 (75.5%) patients with haemophilia A and 72 (24.5%) patients with haemophilia B with a median age at end of follow-up of 54 years (range = 36-90). Mean follow-up duration was 5.86 years. Cardiovascular risk factors were common: hypertension 31.3% (n = 90), diabetes mellitus 10.5% (n = 29), smoking 21.8% (n = 61), obesity 27.6% (n = 69), dyslipidaemia 22.4% (n = 65), family history 8.5% (n = 24), antiretroviral therapy 12.2% (n = 36). There were 24 CVD events (8.2% of the population) with a median age at event of 63 years (range = 46-83). Events consisted of coronary artery disease (CAD), 14; cerebrovascular disease, 4; and atrial fibrillation, 7. CAD was treated with coronary artery bypass grafting in three patients and percutaneous coronary intervention in nine patients. CVD events were complicated by six bleeding events (three minor and three major). CONCLUSION: Cardiovascular disease risk factors and events are relatively common in PWH. PWH can be safely treated for CVD events with similar procedures as the non-PWH populations, though specific clotting factor prophylaxis protocols are not well defined.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".