Sex Differences in Trends in Incidence of Thoracic Aortic Aneurysm Repair and Aortic Dissection: 2005-2015
Bibliographic record
Abstract
Background The purpose of this study was to examine trends in the incidence of thoracic aortic aneurysm (TAA) repair and aortic dissection. Methods A retrospective study was conducted of patients from the period 2005-2015 with thoracic aortic disease. Unadjusted mortality was compared in women vs men. Rates of scheduled TAA repair, dissection events, acute type A aortic dissection (TAAD) repair, and aorta-related mortality were obtained from our institution's clinical registry and administrative data sources and used to calculate the age-adjusted incidence for each sex, adjusted to the Canadian standard population. Weighted linear regression was performed to analyze trends over time. Results A total of 382 scheduled TAA repair operations, 345 dissection events, 85 TAAD repairs, and 182 aorta-related mortalities were identified. Women accounted for 23% of TAA repairs, 39% of dissection events, 22% of TAAD repairs, and 45% of aorta-related mortalities. The incidence of TAA repair was 3.5 per 100,000 person-years (95% confidence interval [CI]: 3.2-3.9), and increased in men ( P = 0.02) but not women ( P = 0.10) over time. The incidence of aortic dissection was 3.4 per 100,000 (95% CI: 3.1-3.8) and was stable over time ( P = 0.43). The average annual age-adjusted incidence of TAAD repair was 0.8 per 100,000 (95% CI: 0.6-1.0) and increased over time ( P = 0.001). The overall incidence of aorta-related mortality was 1.8 per 100,000 (95% CI: 1.5-2.0) and decreased over time ( P = 0.02). Conclusion The incidence of TAA repair is increasing in men but not women. Although aorta-related mortality is decreasing overall, disparities exist between the male and female population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".