Sex Differences in Trends in Incidence of Thoracic Aortic Aneurysm Repair and Aortic Dissection: 2005-2015
Bibliographic record
Abstract
BackgroundThe purpose of this study was to examine trends in the incidence of thoracic aortic aneurysm (TAA) repair and aortic dissection.MethodsA 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.ResultsA 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).ConclusionThe 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".