Bleeding in women with venous thromboembolism during pregnancy: A systematic review of the literature
Bibliographic record
Abstract
Objectives: Venous thromboembolism (VTE) represents an important cause of maternal morbidity and mortality. Estimates of bleeding associated with therapeutic-dose anticoagulation are variable. We describe the frequency of bleeding in pregnant women receiving therapeutic anticoagulation for VTE by means of a systematic review of the literature. Data Sources: Medical Literature Analysis and Retrieval System, Embase, Scopus, Web of Science, and ClinicalTrials.gov were searched. Databases were searched from inception to February 27, 2022. There was no language or geographic location restriction. Methods of Study Selection: The search yielded 2773 articles with 2212 unique citations. Studies were included if they described pregnant women treated for an acute VTE with therapeutic-dose anticoagulation and a defined bleeding outcome was reported. Tabulation Integration and Results: Five studies met inclusion criteria. Included studies were judged to have a serious to critical risk of bias using the Risk of Bias in Nonrandomized Studies of Intervention tool. The rate of bleeding, as defined by respective studies, ranged between 2.9% and 30.0%. Two studies included control groups, one of which found no significant difference in the risk of bleeding between groups, while the other found a significantly increased bleeding risk associated with therapeutic anticoagulation. Conclusion: Among pregnant women anticoagulated for VTE, the reported bleeding risk is variable. The ability to draw definite conclusions is limited by the scarcity and low quality of the studies, the small number of included patients, and the heterogeneity of bleeding definitions used. Large-scale studies with standardized bleeding definitions are required to provide acute bleeding estimates and optimize the care of these patients. Systematic Review Registration: PROSPERO, CRD42021276771.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".