Diagnosis and treatment of von Willebrand disease in 2024 and beyond
Bibliographic record
Abstract
MANUSCRIPT BACKGROUND AND AIM: The diagnosis and clinical care of patients with von Willebrand disease (VWD) has continued to evolve since the characterization of the von Willebrand factor (VWF) gene in 1985. This condition is almost certainly the most common inherited bleeding disorder, and the major symptomatic burden of the disease is experienced by females during their reproductive years. Diagnosis relies on the identification of a personal and family history of excessive mucocutaneous bleeding, and laboratory features consistent with quantitative and/or qualitative abnormalities of VWF. This review focuses on three aspects of VWD management, with current updates and a look into the future. MANUSCRIPT THEMES: First, we will address the role of genetics in the diagnosis and possible therapies for VWD. With current technologies, VWD genetic diagnosis is usually confined to the confirmation of type 2 subtypes of the disease and type 3 VWD analysis for family planning. While type 3 VWD is a potential candidate for the application of gene therapy, no treatments are currently close to entering the clinic. Second, the peri-procedural management of patients with VWD remains an important element of care. The choice of product, its dose and schedule all require careful consideration depending upon the type and disruptive nature of the planned procedure. Lastly, in addition to gene therapy, several other novel therapeutic interventions are also being developed for bleeding and prophylaxis in VWD. These include a VWF aptamer interfering with VWF clearance and bioengineered forms of VWF.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".