Hormonal Contraceptives and Cerebral Venous Thrombosis Risk: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objectives: Use of oral contraceptive pills (OCP) has previously been shown to increase the risk of cerebral venous sinus thrombosis (CVST). Whether this risk varies by type of OCP use, duration of use and other forms of hormonal contraceptives is largely unknown. This systematic review and meta-analysis updates the current state of knowledge on these issues. Methods: We performed a search to identify all published studies on the association between hormonal contraceptive use and risk of CVST in women aged 15-50, using MEDLINE, EMBASE, Cochrane systematic review, the Cochrane Center for Clinical Trials and CINAHL. Risk of CVST was estimated using random effects models. Stratification and meta-regression were used to assess heterogeneity. Results: Of 861 studies reviewed for eligibility, quality, and data extraction, 11 were included in the final systematic review. The pooled odds of developing CVST in women of reproductive age taking oral contraceptives was over 7 times higher compared to women not taking oral contraceptives (OR=7.59, 95% CI 3.82 – 15.09). There is some indication that third generation OCPs may confer a higher risk of CVST than second generation OCPs, but this remains controversial. Data is insufficient to make any conclusions about duration of use and other forms of hormonal contraceptives and risk of CVST. Conclusions: OCP use increases the risk of developing CVST in women of reproductive age. Better studies are needed to determine if duration and type of hormonal contraceptive use modifies this risk.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".