Risk of diabetes in haemophilia patients compared to clinic and non‐clinic control cohorts
Bibliographic record
Abstract
INTRODUCTION: Ageing patients with haemophilia (PWH) develop cardiovascular risk factors impacting care. Little is known about the prevalence of diabetes in PWH and its relation to other comorbidities. AIM: To examine the risk of diabetes for adult PWH compared to men from the general United States population (National Health and Nutrition Examination Surveys [NHANES]) and outpatients attending a Veterans Affairs Medical Center (VAMC) clinic. METHODS: Retrospective cross-sectional design. PWH from four haemophilia centres (n = 690) were matched with random samples from NHANES and VAMC. Diabetes (yes/no) was the outcome, while age, body mass index (BMI), race and Hepatitis C (HCV; by serology) and human immunodeficiency virus (HIV) positivity were covariates. We fitted semiparametric generalized additive models (GAMs) in order to compare diabetes risk between cohorts. RESULTS: Younger PWH were at lower risk of diabetes than NHANES or VAMC subjects irrespective of BMI. However, the risk of diabetes rose in older PWH and was closely associated with HCV. For HCV-negative subjects, the risk of diabetes was considerably lower for PWH than NHANES and VAMC subjects. The difference persisted after controlling for BMI and age, indicating that the low risk of diabetes in PWH cannot be explained by lean body mass alone. CONCLUSION: Since many ageing PWH are HCV positive and therefore at heightened risk for diabetes, it is important to incorporate diabetes screening into care algorithms in Haemophilia Treatment Centers, especially since PWH are not always followed in primary care clinics.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".