Gynecologic and obstetric management of women with von Willebrand disease: summary of 3 systematic reviews of the literature
Bibliographic record
Abstract
von Willebrand disease (VWD) disproportionately affects women because of the potential for heavy menstrual bleeding (HMB), delivery complications, and postpartum hemorrhage (PPH). To systematically synthesize the evidence regarding first-line management of HMB, treatment of women requiring or desiring neuraxial analgesia, and management of PPH. We searched Medline and EMBASE through October 2019 for randomized trials, comparative observational studies, and case series comparing the effects of desmopressin, hormonal therapy, and tranexamic acid (TxA) on HMB; comparing different von Willebrand factor (VWF) levels in women with VWD who were undergoing labor and receiving neuraxial anesthesia; and measuring the effects of TxA on PPH. We conducted duplicate study selection, data abstraction, and appraisal of risk of bias. Whenever possible, we conducted meta-analyses. We assessed the quality of the evidence using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. We included 1 randomized trial, 3 comparative observational studies, and 10 case series. Moderate-certainty evidence showed that desmopressin resulted in a smaller reduction of menstrual blood loss (difference in mean change from baseline, 41.6 [95% confidence interval, 16.6-63.6] points in a pictorial blood assessment chart score) as compared with TxA. There was very-low-certainty evidence about how first-line treatments compare against each other, the effects of different VWF levels in women receiving neuraxial anesthesia, and the effects of postpartum administration of TxA. Most of the evidence relevant to the gynecologic and obstetric management of women with VWD addressed by most guidelines is very low quality. Future studies that address research priorities will be key when updating such guidelines.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".