Prevalence of Cardiovascular Risk Factors in Women With Obstructive Coronary Disease Requiring Revascularization: A Meta-analysis
Bibliographic record
Abstract
Introduction Cardiovascular disease continues to be the first cause of premature mortality in women, who have been previously overlooked in clinical trials. Several studies showed that women undergoing coronary artery bypass grafts (CABG) and percutaneous coronary intervention (PCI) present more cardiovascular risk factors at baseline, develop more post-procedural complications and have higher mortality than men. The goal of this review is to analyze the difference between men and women in terms of the prevalence of individual cardiovascular risk factors. Methods A meta-analysis of original investigations with adult subject who underwent surgical or percutaneous coronary interventions in which cardiovascular risk factors were evaluated and was conducted using Medline, Cochrane, EBM review, Ovid Embase, Google Scholar and PubMed databases. Results Of the 4567 identified records found, 18 were retained for qualitative analysis. Prevalence of hypertension (CABG: 71% (64%, 78%); PCI:(59% (48%,70%)) and diabetes (CABG: 48% (38%,57%); PCI 43% (27%, 59%)) was high in women. Women who underwent either CABG and PCI had a higher odd of hypertension (CABG: OR (1.92 [1.47-2.50], p<0.05); PCI: (1.86 [1.76-1.97], p<0.05)), and diabetes (CABG: (1.94 [1.55-2.42], p<0.05; PCI: (1.97 [1.54-2.53], p<0.05)). However, the prevalence of smoking compared to men was lower (CABG: (0.17 [0.06-0.52], p<0.05; PCI: (0.22 [0.06-0.86], p<0.03)). Conclusion The studies showed that women who underwent either surgical or percutaneously revascularization had higher odds of hypertension and diabetes compared to men.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.048 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".