Challenges in the management of women with type 2B von Willebrand disease during pregnancy and the postpartum period: evidence from literature and data from an international registry and physicians’ survey—communication from the Scientific and Standardization Committees of the International Society on Thrombosis and Haemostasis
Bibliographic record
Abstract
BACKGROUND: Management of women with type 2B von Willebrand disease (VWD) during pregnancy is challenging because of dysfunctional von Willebrand factor (VWF) and the complexity resulting from discrepant VWF/factor VIII (VWF/FVIII) levels, impaired platelet-dependent VWF activity, progressive thrombocytopenia, and risks associated with the use of desmopressin. There is a lack of high-quality evidence to support clinical decision making. OBJECTIVES: In this study, we examined the current diagnostic and management approaches and outcomes in women with VWD during pregnancy. METHODS: Data were collected via 3 avenues: literature review, an international registry, and an international survey on physicians' practices for the management of pregnancy in women with VWD. The registry and survey were supported by the International Society on Thrombosis and Haemostasis. RESULTS: Data on clinical and laboratory features, management and bleeding complications, and pregnancy outcomes of a total of 55 pregnancies from 49 women across the globe (literature: 35, registry: 20) and data reported by 112 physicians were analyzed. We describe the largest dataset on pregnancies in women with type 2B VWD available to date. The data highlight the following key issues: a) bleeding complications remain a concern in these patients, b) the target safe VWF level and the ideal monitoring approach are unknown, c) there is a wide range of hemostatic management practices in the type and timing of treatment, and d) physicians have diverse views on the mode of delivery and use of neuraxial anesthesia. CONCLUSION: We conclude that an international consensus and guidance are critically required for better care and improved outcomes in this patient cohort.
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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 | 0.016 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".