Risk Factors for Venous Thromboembolism in Endometrial Cancer
Bibliographic record
Abstract
Background: Venous thromboembolism (VTE) in malignancy is associated with poor outcomes. We conducted a retrospective review of VTE in patients with endometrial cancer to characterize the VTE incidence, identify factors that contribute to VTE risk, and compare survival outcomes in patients with and without VTE. Methods: A retrospective chart review identified 422 eligible patients who underwent surgery for endometrial cancer (1 January 2014 to 31 July 2016). The primary outcome was VTE. Binary logistic regression identified risk factors for VTE; significant risk factors were included in a multivariate analysis. Kaplan–Meier estimates are reported, and log rank tests were used to compare the Kaplan–Meier curves. Risk-adjusted estimates for overall survival based on VTE were determined using a multivariate Cox proportional hazards model. Results: The incidence of VTE was 6.16% overall and 0.7% within 60 days postoperatively. Non-endometrioid histology, stages 3 and 4 disease, laparotomy, and age (p < 0.1) were identified as factors associated with VTE and were included in a multivariate analysis. The overall death rate in patients with VTE was 42% (9% without VTE): hazard ratio, 5.63; 95% confidence interval, 2.86 to 11.08; p < 0.0001. Adjusting for age, stage of disease, and histology, risk of death remained significant for patients with a VTE: hazard ratio, 2.20; 95% confidence interval, 1.09 to 4.42; p = 0.0271. Conclusions: A method to identify patients with endometrial cancer who are at high risk for VTE is important, given the implications of VTE for patient outcomes and the frequency of endometrial cancer diagnoses. Factors identified in our study might assist in the recognition of such patients.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".