Risk of Venous Thromboembolic Events in Pregnant Women With Cancer [311]
Bibliographic record
Abstract
INTRODUCTION: Venous thromboembolism is one of the leading causes of pregnancy-associated death in the western world. Cancer is a known risk factor for thrombosis outside of pregnancy. The objective of this study is to evaluate the effect of cancer on the risk of venous thromboembolism in pregnancy. METHODS: We conducted a retrospective population-based cohort study using the Health Care Cost and Utilization Project, Nationwide Inpatient Sample database from 2003 to 2011. We classified cancers according to location and estimated the risk of developing venous thromboembolism among pregnant women with the 10 most prevalent malignancies using unconditional logistic regression analysis. RESULTS: There were 7,917,453 births in our cohort of which 2,826 were to women with underlying malignancies. Risk of venous thromboembolism among women with no malignancy was 7.22 per 10,000 births. This risk was considerably increased among women with cervical cancer (odds ratio [OR] 8.64, 95% confidence interval [CI] 2.15–34.79), ovarian cancer (OR 10.35, 95% CI 1.44–74.19), Hodgkin's disease (OR 7.87, 95% CI 2.94–21.05), and myeloid leukemia (OR 20.75, 95% CI 6.61–65.12). CONCLUSION: Many cancers increase risk of venous thromboembolism in pregnancy. In light of this risk, thromboprophylaxis should be considered for all women with an underlying malignancy.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".