Pregnancy-Associated Venous Thromboembolism: Insights from GARFIELD-VTE
Bibliographic record
Abstract
Abstract Introduction The risk of venous thromboembolism (VTE) increases during pregnancy and the puerperium such that VTE is a leading cause of maternal mortality. Methods We describe the clinical characteristics, diagnostic strategies, treatment patterns, and outcomes of women with pregnancy-associated VTE (PA-VTE) enrolled in the Global Anticoagulant Registry in the FIELD (GARFIELD)-VTE. Women of childbearing age (<45 years) were stratified into those with PA-VTE (n = 183), which included pregnant patients and those within the puerperium, and those with nonpregnancy associated VTE (NPA-VTE; n = 1,187). Patients with PA-VTE were not stratified based upon the stage of pregnancy or puerperium. Results Women with PA-VTE were younger (30.5 vs. 34.8 years), less likely to have pulmonary embolism (PE) (19.7 vs. 32.3%) and more likely to have left-sided deep vein thrombosis (DVT) (73.9 vs. 54.8%) compared with those with NPA-VTE. The most common risk factors in PA-VTE patients were hospitalization (10.4%), previous surgery (10.4%), and family history of VTE (9.3%). DVT was typically diagnosed by compression ultrasonography (98.7%) and PE by chest computed tomography (75.0%). PA-VTE patients more often received parenteral (43.2 vs. 15.1%) or vitamin K antagonists (VKA) (9.3 vs. 7.6%) therapy alone. NPA-VTE patients more often received a DOAC alone (30.2 vs. 13.7%). The risk (hazard ratio [95% confidence interval]) of all-cause mortality (0.59 [0.18–1.98]), recurrent VTE (0.82 [0.34–1.94]), and major bleeding (1.13 [0.33–3.90]) were comparable between PA-VTE and NPA-VTE patients. Uterine bleeding was the most common complication in both groups. Conclusion VKAs or DOACs are widely used for treatment of PA-VTE despite limited evidence for their use in this population. Rates of clinical outcomes were comparable between groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".