Von Willebrand Disease: Gaining a global perspective
Bibliographic record
Abstract
INTRODUCTION: Recent guidelines for von Willebrand Disease (VWD) highlighted the challenges in diagnosis and management. Identifying the number of persons with VWD (PwVWD) internationally will help target support to aid diagnosis of PwVWD. AIM: To examine international registration rates of PwVWD, the influence of income status, geographical region and the age and sex profile. Cumulatively, these data will be used to inform future strategy from the World Federation of Haemophilia (WFH) to address unmet clinical and research needs. METHODS: Data from the 2018/2019 WFH Annual Global Survey (AGS) were analysed, providing a global perspective on VWD registration. RESULTS: Registration rates are lowest in South Asia (0.6/million population) and highest in Europe/Central Asia (50.9/million population, 0.005%), but below the expected prevalence rate (0.1%). National economic status impacted VWD registration rates, reflecting variation in access to optimal healthcare infrastructure. Females represented the majority of PwVWD globally, however, in low-income countries (LIC) males predominated. Age profile varied, with markedly higher rates of paediatric registrations in North America, Middle East and North Africa and South Asia. Rates of type 3 VWD registrations were significantly influenced by economic status (81% of VWD diagnoses in LIC), suggesting only the most severe VWD types are diagnosed in resource limited settings. CONCLUSION: Significant variation in registration rates of PwVWD exist internationally and is influenced by income status and the presence of HTC networks. Improved understanding of registration rates will enable targeting of advocacy to improve awareness, diagnosis and support for PwVWD internationally. KEY POINTS: Registration rates of People with Von Willebrand Disease (PwVWD) vary internationally and are influenced by national income status Although females represent the majority of PwVWD globally, in low income countries (LIC) males predominated, possibly related to stigma surrounding gynaecological bleeding. Rates of type 3 VWD registration were significantly influenced by economic status (81% of VWD diagnoses in LIC), suggesting only the most severe VWD types are diagnosed in resource limited settings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".