Venous Thromboembolism in Pregnancy [39L]
Bibliographic record
Abstract
INTRODUCTION: Venous thromboembolism is one of the leading causes of pregnancy-related maternal death in developed countries. The incidence rate is much higher during the postpartum period with an overall incidence of 2.5 to 9.9 per 1000 METHODS: The data were obtained from the 1999-2015 Health Care Cost and Utilization Project, Nationwide Inpatient Sample (HCUP-NIS) and used to carry out a population-based cohort study. The purpose of our study is to trend the rate of VTE during pregnancy and postpartum period and the assessment of the risk factors using a large population-based administrative database from the United States. Logistic regression was used to calculate the odds ratio (OR) and corresponding 95% confidence intervals (CIs) of demographic and obstetrical determinants of VTEs. RESULTS: The overall incidence of VTE was in an upward trend until 2015 when it reached 20/10,000 pregnancies, with an average case fatality rate of 1.16%. VTE is associated with maternal age above 25, anemia, heart disease, hypertension, obesity, transfusion, C-section, postpartum hemorrhage and PET. Predictors of VTE fatalities included obesity, hypertension, PET, postpartum hemorrhage, PTB and DM. CONCLUSION: VTE is a condition with an increasing in incidence and is associated with a significant maternal morbidity and mortality. Multiple risk factors associated with VTE with obesity being the most significant in our study. Further research targeting risk factors and prevention in pregnancy is warranted.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".