Diabetes, hepatitis C and human immunodeficiency virus influence hypertension risk differently in cohorts of haemophilia patients, veterans and the general population
Bibliographic record
Abstract
INTRODUCTION: The reasons for the high prevalence of hypertension in persons with haemophilia (PWH) are poorly understood. AIM: To examine the roles of diabetes, Hepatitis C Virus (HCV) and Human Immunodeficiency Virus (HIV) in the etiology of hypertension for PWH. METHODS: Retrospective cross-sectional design. Adult PWH (n = 691) were divided into two groups: (A) free of diabetes, HCV and HIV; (B) with diabetes and/or HCV positivity and/or HIV positivity. Each group was matched by race and age with random samples from the general population of the US (National Health and Nutrition Examination Surveys, NHANES) and outpatients at the Veterans Affairs Medical Center (VAMC) in San Diego. Generalized additive models (GAMs) were fitted for graphical analysis of hypertension risk over the lifespan. RESULTS: In Group A, PWH had the highest prevalence of hypertension compared to NHANES and VAMC, especially in young adults. In Group B, diabetes increased the risk of hypertension for all three cohorts (PWH, NHANES and VAMC), especially for PWH. In PWH, hypertension risk was also increased by HIV, in NHANES by HCV, and in VAMC by HCV and HIV. CONCLUSION: Diabetes conferred the greatest risk of hypertension for all three cohorts. However, curves of hypertension in relation to age revealed that diabetes, HCV and HIV modulated hypertension risk differently in PWH. PWH experienced a disproportionally high risk increase with diabetes. Therefore, haemophilia care should include screening for hypertension and diabetes at a young age.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".