Non-malignant gynaecological disease and risk of cardiovascular or cerebrovascular disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Cardiovascular disease is the leading cause of death globally. Non-malignant gynaecological diseases (NMGD) significantly affect patient health and well-being and may be associated with cardiovascular or cerebrovascular disease (C/CVD). Methods Seven databases were searched for relevant studies up to 21 April 2024. Observational studies reporting risk estimates and 95% CIs for the association between NMGD and C/CVD were included. Data were extracted by two independent reviewers. Random effects models were used to calculate summary relative risk (SRR) with 95% CI. Composite C/CVD outcome was defined as a combination of ischaemic heart disease, cerebrovascular disease, heart failure, and peripheral vascular disease. The ROBINS-I tool defined study quality and risk of bias. Results We screened 6639 studies, of which 59 were eligible for full-text review and 28 were included in our analysis, comprising a total of 3 271 242 individuals. The majority (53.5%) of the studies were scored as having a ‘serious’/‘critical’ risk of bias. Overall, individuals with an NMGD had a significantly greater risk of composite C/CVD with low heterogeneity among contributing studies (SRR 1.28, 95% CI 1.20 to 1.37; n=16 studies, I2=65.3%), ischaemic heart disease (SRR 1.41, 95% CI 1.31 to 1.51; n=21 studies, I2=73.7%), and cerebrovascular disease (SRR 1.33, 95% CI 1.18 to 1.51; n=16 studies, I2=91.5%). In NMGD-specific analyses, the risk of C/CVD and its components was greater among those with a history of endometriosis or polycystic ovary syndrome. Conclusions We found an overall association between NMGD and C/CVD across all studies. However, estimates from individual studies varied substantially.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".