Unravelling the Causal Relationship between Endometriosis and the Risk for Developing Venous Thromboembolism: A Pooled Analysis
Bibliographic record
Abstract
To investigate the effect of endometriosis on venous thromboembolism (VTE) in oral contraceptive (OC) users. Pooled analysis on a harmonized dataset compromising international patient-centric cohort studies: INAS-VIPOS, INAS-SCORE, and INAS-FOCUS. Eleven European countries, the United States, and Canada. Individuals being newly prescribed an OC with or without an endometriosis and no VTE history.Detailed information was captured using self-administered questionnaires at baseline and every 6 to 12 months thereafter. Self-reported VTEs were medically validated and reviewed by an independent adjudication committee. Incidence rates (IRs) were calculated per 10,000 woman-years. The association of endometriosis on VTE was determined in a time-to-event analysis, calculating crude and adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) using stabilized inverse probability of treatment weighting (IPTW).A total of 22,072 women had an endometriosis diagnosis, and 91,056 women did not. Women with endometriosis contributed 78,751 woman-years during which 41 VTE events occurred (IR: 5.2/10,000, 95% CI: 3.7-7.1) compared to 127 VTEs during 310,501 woman-years in women without endometriosis (IR: 4.1/10,000, 95% CI: 3.4-4.9). The hazard ratio of VTE in women with endometriosis was 1.79 (95% CI: 1.24-2.57) using stabilized IPTW controlling for age, body mass index, smoking, education, age at menarche, and family history of VTE. Subgroup and sensitivity analyses showed similar results.These results highlight the importance of considering endometriosis as a potential factor contributing to VTE in women using OC; however, further research on the relationship between endometriosis and VTE 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 | 0.029 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.023 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".