Blood pressure in persons with haemophilia with a focus on haemophilia‐specific risk factors
Bibliographic record
Abstract
INTRODUCTION: Persons with haemophilia (PWH) have a higher prevalence of hypertension compared to the general population, which cannot be explained entirely by the usual cardiovascular risk factors. Neutralizing antibodies (inhibitors) against clotting factors might have some relation to cardiovascular disease in PWH. However, whether inhibitors facilitate hypertension is unknown. AIM: We investigated the relationship between hypertension/blood pressure and inhibitors in PWH. Additional goals were to determine the relationships with haemophilia type, race, and viral status. METHODS: Records were extracted retrospectively for PWH (age ≥18 years) between 2003 and 2014 from four Hemophilia Treatment Centers in North America and included demographics, weight, height, haemophilia type/severity, HCV and HIV infection status, hypertension, use of anti-hypertensive medications, and inhibitor status. We fitted semiparametric generalized additive models (GAMs) to describe adjusted curves of blood pressure (BP) against age. RESULTS: Among 691 PWH, 534 had haemophilia A and 157 had haemophilia B, with a median age of 39 years (range 18 to 79). Forty-four PWH (6.5%) had a history of inhibitors, without evidence for a higher prevalence of hypertension or higher BP. A higher prevalence of hypertension and higher BP were noted for haemophilia A (vs. haemophilia B), coinfection with HCV/HIV (vs. uninfected), or moderate haemophilia (vs. severe haemophilia). CONCLUSION: While there was no signal to suggest that a history of inhibitors is associated with hypertension, differences based on haemophilia type, severity, and viral infection status were identified, encouraging prospective investigations to better delineate haemophilia-specific risk factors for hypertension.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".