Meta-Analysis of VTE Risk: Ovarian Cancer Patients by Stage, Histology, Cytoreduction, and Ascites at Diagnosis
Bibliographic record
Abstract
Venous thromboembolisms (VTEs) have been a leading secondary cause of death among ovarian cancer patients, prompting multiple studies of risk factors. The objective of this meta-analysis is to quantify the associations between VTE and the most commonly reported risk factors among ovarian cancer patients. PubMed, Embase, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were used to identify observational studies. Two reviewers independently abstracted data and assessed quality via the Newcastle–Ottawa tool. A random effects model was used to calculate the pooled odds ratios for VTE with each of the following exposures: advanced cancer stage, clear cell histology, serous histology, ascites at diagnosis, and complete cytoreduction. The I2 and Q tests were used to evaluate heterogeneity. Twenty cohort studies with 6,324 total ovarian cancer patients, 769 of whom experienced a VTE, were included. The odds of VTE in ovarian cancer patients were higher among patients with cancer stage III/IV (versus cancer stage I/II, pooled odds ratio (OR) 2.73; 95% CI 1.84–4.06; I2= 64%), clear cell (versus nonclear cell) histology (OR 2.11; 95% CI 1.55–2.89; I2 = 6%), and ascites (versus no ascites) at diagnosis (OR 2.12; 95% CI 1.51–2.96; I2 = 32%). Serous (versus nonserous) histology (OR 1.26; 95% CI 0.91–1.75; I2 = 42%) and complete (versus incomplete) cytoreduction (OR 1.05; 95% CI 0.27–4.11; I2 = 88%) were not associated with VTE. This meta-analysis quantifies the significantly elevated odds of VTE in ovarian cancer patients with advanced stage at diagnosis, clear cell histology, and ascites at diagnosis. Further studies are needed to account for confounders and inform clinical decision-making tools.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.068 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".