Reported prevalence of von Willebrand disease worldwide in relation to income classification
Bibliographic record
Abstract
INTRODUCTION: The diagnosis of von Willebrand disease (VWD) is complex and challenging, especially when diagnostic resources are limited. This results in a lack of consistency in identifying and reporting the number of people with VWD and variations in the VWD prevalence worldwide. AIM: To analyze the reported prevalence of VWD worldwide in relation to income classification. METHODS: Data on the VWD prevalence from the World Federation of Hemophilia Annual Global Survey, national registries of Australia, Canada, and the United Kingdom, and the literature were analysed. The income level of each country was classified according to the World Bank. RESULTS: The mean VWD prevalence worldwide was 25.6 per million people. The VWD prevalence for high-income countries (HIC) of 60.3 per million people was significantly greater (p < .01) than upper middle (12.6), lower middle (2.5) and low (1.1) income countries. The type 3 VWD prevalence for HIC of 3.3 per million people was significantly greater (p < .01) than lower middle (1.3) and low income (0.7) countries. The reported VWD prevalence was greater among females than males. CONCLUSION: The reported VWD prevalence varied considerably across and within income classifications. The variability of type 3 VWD prevalence was less than the VWD prevalence (all types). The variability in detection and diagnosis of type 1 VWD presents a challenge in forming a consistent prevalence value across countries and income classifications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".