Disparities in mortality rates from aortic aneurysm and dissection by country-level income status and sex
Bibliographic record
Abstract
Objective To investigate the impact of national income level and sex on mortality trends from aortic aneurysm and dissection in addition to all aortic disease as a whole. Methods Using data from the World Health Organization mortality database, we conducted an analysis of mortality trends from aortic disease between 2000 and 2019, Countries were categorized into middle-income and high-income countries (MICs and HICs) on the basis of income level. Age-standardized and sex-specific age-standardized mortality rates per 100,000 persons, along with male-to-female mortality ratios, were calculated. Trends over the study period were analyzed using joinpoint regression. Results Our analysis comprised 29 MICs and 46 HICs, with an average population of 595 million and 1042 million during the observation period. During the observation period, age-standardized mortality rates from aortic disease decreased to 2.21 (2.17-2.25) and 2.28 (2.26-2.30) in MICs and HICs, respectively (average annual percentage change of −0.5% in MICs and −1.8% in HICs, P < .05 for both). However, mortality rates from aortic dissection increased in HICs from 2000 to 2019 (average annual percentage change of 1.3%, P < .001). Mortality from aortic disease, aortic dissection, and aortic aneurysm were male dominant in MICs and HICs but decreasing trends during the observation periods except for aortic dissection in MICs. Conclusions We present the contemporary and comprehensive analysis of global socioeconomic status and aortic diseases mortality. Although trends of mortality from aortic diseases are on the decline in both MICs and HICs, there is a striking increase in mortality for aortic dissection, specifically in HICs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".