What have we learned about the patient's experience of von Willebrand disease? A focus on women
Bibliographic record
Abstract
Von Willebrand disease (VWD), the most common inherited bleeding disorder (IBD), disproportionately affects females, given the hemostatic challenges they may encounter throughout their lifetimes. Despite this, research about VWD remains grossly underrepresented, particularly compared to hemophilia, which is historically diagnosed in males. Structural sexism, stigmatization of menstrual bleeding, delayed diagnosis, and a lack of timely access to care result in an increased frequency of bleeding events, iron deficiency, iron deficiency anemia, and a decreased quality of life. However, we are only beginning to recognize and acknowledge the magnitude of the burden of this disease. With an increasing number of studies documenting the experiences of women with IBDs and recent international guidelines suggesting changes to optimal management, a paradigm shift in recognition and treatment is taking place. Here, we present a fictional patient case to illustrate one woman's history of bleeding. We review the evidence describing the impact of VWD on quality of life, normalization of vaginal bleeding, diagnostic delays, and the importance of access to multidisciplinary care. Furthermore, we discuss considerations around reproductive decision-making and the intergenerational nature of bleeding, which often renders patients as caregivers. Through incorporating the patient perspective, we argue for an equitable and compassionate path to overcome decades of silence, misrecognition, and dismissal. This path moves toward destigmatization, open dialogue, and timely access to specialized care.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".