Patient-centered care in von Willebrand disease: are we there yet?
Bibliographic record
Abstract
INTRODUCTION: Von Willebrand Disease is the most common inherited bleeding disorder. Paradoxically, affected individuals are often misdiagnosed and experience substantial diagnostic delay. There are sex-specific health disparities in VWD rooted in the stigmatization of vaginal bleeding, which leads to symptom dismissal, lack of timely access to care and lower health-related quality of life. AREAS COVERED: Following the core elements of patient-centered care - respect for patient preferences, values, and needs, we describe the current state of VWD care. Challenges of diagnostic delay, serial misrecognition of abnormal bleeding, and symptom dismissal are barriers that disproportionately affect women with VWD. These negative effects are further amplified in individuals living in low- and middle-income countries. We describe the importance of coordinated multidisciplinary care, as well as the need for patient education and empowered self-advocacy. EXPERT OPINION: While tremendous work has been done to improve the diagnosis and management of VWD, timely and high-quality research is urgently needed to address care gaps. Systemic changes such as resource investment, dedicated research funding for novel treatment modalities, and effective knowledge translation strategies to address structural barriers are needed to facilitate effective patient-centered care for VWD.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".