Pregnancy loss in individuals with von Willebrand disease and unspecified mucocutaneous bleeding disorders: a multicenter cohort study
Bibliographic record
Abstract
BACKGROUND: While bleeding around pregnancy is well described in von Willebrand disease (VWD), the risk of pregnancy loss is less certain. OBJECTIVES: We aimed to describe the frequency of pregnancy loss in females with VWD compared with those with a similar mucocutaneous bleeding phenotype and no VWD or compared with nonbleeding disorder controls. METHODS: Female patients were consecutively approached in 8 specialty bleeding disorder clinics between 2014 and 2023. The VWD group was defined as having von Willebrand factor (VWF) antigen and VWF activity levels, each <0.50 IU/mL on ≥2 occasions, and a condensed MCMDM-1 score of ≥4. The non-VWD mucocutaneous bleeding disorder group had VWF levels ≥ 0.50 IU/mL on ≥2 occasions and an MCMDM-1 score ≥ 4. A nonbleeding disorder control group was recruited in pregnancy from a low-risk maternity clinic. RESULTS: There were 150 females in the VWD group, 145 in the non-VWD mucocutaneous bleeding disorder group, and 137 in the control group. There was a similar frequency of individuals with ≥1 loss in the VWD group (45.3%, 68/150), the non-VWD group (56.6%; 82/145; -11.2%; 97.5% CI, -24.2%, 1.8%), and the nonbleeding disorder control group (37.2%; 51/137; 8.1%; 97.5% CI, -4.9%, 21.1%). Using a logistic regression, the odds ratio of pregnancy losses in the VWD group vs the non-VWD group was 0.94 (95% CI 0.65, 1.36). All groups experienced more recurrent losses compared with the literature. CONCLUSION: There was no statistically significant difference in risk of pregnancy loss between females with VWD, females with a similar mucocutaneous bleeding phenotype, and nonbleeding disorder controls.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".