Sex differences in acute type A aortic dissection: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The objective of this study is to provide a comprehensive comparison of outcomes following acute type A aortic dissection (ATAAD) repair in males and females.EVIDENCE ACQUISITION: PubMed, Medline, and Web of Science were systematically searched by two authors for studies published from January 1st, 2000, to May 10th, 2021. Overall, 2405 articles were screened, and 16 were included in this review. Meta-analysis of the compiled data was performed.EVIDENCE SYNTHESIS: Pooled estimates indicated no difference in operative (odds ratio (OR) 1.00, 95% confidence interval (CI) 0.59-1.67, P=0.99, I2=52%), in-hospital (OR 0.78, 95% CI 0.56-1.08; P=0.13, I2=57%), and 30-day mortality (OR 1.09, 95% CI 0.83-1.43, P=0.52, I2=45%) between the sexes. However, males had significantly reduced 5-year mortality rates (OR 0.71, 95% CI 0.51-1.00, P=0.05, I2=45%). There was no difference between sexes in rates of postoperative stroke (OR 1.07, 95% CI 0.86-1.33, P=0.54, I2=0%), atrial fibrillation (OR 0.99, 95% CI 0.82-1.19, P=0.92, I2=0%), as well as mesenteric or limb ischemia (OR 0.73, 95% CI 0.22-2.43, P=0.61, I2=77%; OR 0.83, 95% CI 0.30-2.30, P=0.72, I2=76%, respectively). Males did experience significantly increased rates of acute renal failure and reoperation (OR 1.35, 95% CI 1.16-1.56, P=0.0001, I2=29%; OR 1.40, 95% CI 1.09-1.81, P=0.010, I2=42%).CONCLUSIONS: Composite analysis indicates that early mortality does not differ between the sexes; however, late outcomes favor males. Differences in preoperative presentation and subsequent procedure selection between the sexes likely contribute to the disparity in late outcomes. Decision-making for surgical treatment of ATAAD should account for sex-specific risk factors.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.005 | 0.006 |
| 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".