Sex-related differences in myocardial fibrosis among patients with aortic stenosis: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: -analysis aimed to evaluate sex-related differences in myocardial fibrosis using cardiac magnetic resonance (CMR) parameters. Methods: Three databases were searched for studies comparing male and female patients with AS reporting CMR-derived measures. The primary outcomes were late gadolinium enhancement (LGE%), infarct-related and non-infarct-related LGE, extracellular volume (ECV) and Septal E/e'. Effect sizes were expressed as risk ratios (RR) for binary outcomes and mean differences (MD) for continuous outcomes, each with 95% confidence intervals (CI), using random-effects models. Study quality was appraised with the Newcastle-Ottawa Scale, and certainty of evidence was graded using the GRADE framework. Results: Seven studies (n = 2,105; 1,246 males) were included. No significant difference was observed in LGE% (MD 0.13; 95 %CI -0.93 to 1.18; p = 0.770), and risks of infarct-related LGE between sexes (RR 1.61; 95 %CI 0.90 to 2.89; p = 0.080). Males had higher risk of non-infarct LGE (RR 1.51; 95 %CI 1.34 to 1.70; p = 0.002). There were no significant differences in ECV (MD -0.45; 95 %CI -2.34 to 1.44; p = 0.506) and Septal E/e' between sexes (MD -1.87; 95 %CI -4.05 to 0.32; p = 0.072). Conclusion: -analysis shows sex-related differences in myocardial fibrosis in AS, with men exhibiting more focal replacement fibrosis and women a tendency toward diffuse interstitial fibrosis. These patterns highlight the relevance of incorporating sex-specific factors into diagnosis and management.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".