New and emerging therapies for women, girls, and people with the potential to menstruate with VWD
Bibliographic record
Abstract
Innovation in therapies for patients with von Willebrand disease (VWD) has lagged far behind that for hemophilia, creating inequity in the bleeding disorder community. Although currently existing treatments of antifibrinolytics, desmopressin, and plasma-derived von Willebrand factor replacement are considered effective, multiple studies report poor quality of life in patients with VWD, especially those with heavy menstrual bleeding (HMB). This disconnect underscores the need for novel therapies that are safe and effective and that consider a patient's specific contraceptive and reproductive needs. Recombinant von Willebrand factor is the most recent new therapy for VWD; the data specific to women are reviewed. We also present emerging data on emicizumab for the treatment of VWD, BT200 (rondoraptivon pegol), generalized hemostatic therapies (VGA039 and HMB-011), as well as treatments based on nanotechnology (platelet-inspired nanoparticles and KB-V13A12). We are optimistic as we move toward pivotal clinical trials for these elegant and innovative treatments.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".