The impact of malignancy on the risk of venous thromboembolism in pregnant women: a systematic review
Bibliographic record
Abstract
Background and aims: Venous thromboembolism (VTE) is a common complication of malignancy associated with a three-fold increase risk of death. Pregnancy is also a recognized risk factor for VTE, and is associated with a 4-5 fold increase risk compared to non-pregnant women. Considering that any review article has not been published in this field yet and given that complications of VTE can be reduced by early identifying between pregnant women the current systematic review aimed to elucidate the impact of malignancy on the risk of VTE in pregnant females. Methods: We carried out a systematic search in multiple databases, including PubMed (Medline), Google Scholar, and Scopus up to January 2023. Finally, 441 related articles were extracted from the databases, after screening the title, abstract and full text, seven articles were included in the study. Results: Seven studies (6 cohorts and 1 cross-sectional) with an entire of 58,854,195 pregnant females (22,396 cancer patients) were included. These studies were done in the United States of America, Canada, Brazil, and Denmark. All of the studies except one study demonstrated that cancer in pregnant patients increased the risk of deep vein thrombosis (DVT). The VTE prevalence was significantly higher in cancer groups compared with the non-cancer group and the highest (adjusted odds ratio) aOR was correlated to myeloid leukemia. Conclusions: Pregnant women with malignancy are more susceptible to VTE and other coagulation disorders. Physicians and health policymakers should be of high vigilance to pregnancy-associated VTE, especially in women suffering from cancer.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".