Global prevalence of platelet-type von Willebrand disease
Bibliographic record
Abstract
Background Platelet-type von Willebrand disease (PT-VWD) is a rare autosomal dominant disorder. It is caused by gain-of-function gene variants in the platelet GP1BA , which results in excessive binding between GPIbα and von Willebrand factor (VWF). The prevalence of PT-VWD is unknown. Objectives To establish the worldwide and within distinct ethnic groups prevalence of PT-VWD. Methods We used available exome and genome sequencing data of 807,162 (730,947 exomes and 76,215 genomes) subjects from the Genome Aggregation Database (gnomAD-v4.1). Results Among the 1,614,324 alleles analyzed in the gnomAD population, there were 1397 distinct GP1BA variants. Of them, 4 variants (p.Arg127Gln, p.Leu194Phe, p.Gly249Val, and p.Met255Ile) have been previously reported to cause PT-VWD. Considering these 4 known pathogenic variants, we estimated a global PT-VWD prevalence of 136 cases/10 6 . The highest estimated prevalence of PT-VWD was found in Africans/African Americans (160/10 6 ), Finnish (156/10 6 ), Europeans (149/10 6 ), and South Asians (110/10 6 ), followed by Ashkenazi Jewish (68/10 6 ) and East Asian (45/10 6 ) ethnicities. In the population with no assigned ethnicity, a prevalence of 126/10 6 was estimated. Since no pathogenic GP1BA variants that were previously reported to cause PT-VWD were found in Admixed American and Middle Eastern ethnicities, we were unable to estimate the PT-VWD prevalence in these 2 populations. We found a global prevalence of 2.5/10 6 for severe PT-VWD and 134/10 6 for the mild form. Conclusion This population-based genetic epidemiology analysis indicates a substantially higher than expected frequency of PT-VWD. This novel finding suggests that a large number of PT-VWD patients are still under- or misdiagnosed.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".