Trends in abdominal aortic aneurysm mortality in European Union 15+ Countries from 1990-2017
Bibliographic record
Abstract
Background: This observational study assesses trends in abdominal aortic aneurysm (AAA) death rates in European Union (EU) 15+ countries for the years 1990-2017. Methods: Age-standardised death rates were extracted from the Global Burden of Disease Study Global Health Data Exchange. Trends were analysed using Joinpoint regression analysis. Results: Between 1990 and 2017, age-standardised death rates (ASDR) from AAA decreased in all 19 EU15+ countries for females, and in 18 of 19 countries for males: Increasing AAA mortality was observed only for males in Greece (+5.3%). The largest relative decreases in ASDR between 1990-2017 were observed in Australia (males -65.6%, females -50.4%) and Canada (males -60.8%, females -48.6%). Over the 28-year period for females, the smallest decreases in ASDR were observed in Greece (-2.3%) and in Italy (-2.5%). In 2017, the highest mortality rates were observed in the United Kingdom for both males and females (7.5/100,000 and 3.7/100,000 for males and females, respectively). The lowest ASDR was observed in Portugal for males (2.8/100,000) and Spain for females (1.0/100,000). ASDRs for AAA in 2017 were higher in males than females in all 19 EU15+ countries. The most recent trends demonstrated increasing AAA ASDRs in 14 of 19 countries for both males and females; the increases were relatively small when compared with the improvements observed within the preceding years. Conclusion: In this observational study of EU15+ countries, we identify decreasing mortality from AAA across EU15+ countries since 1990. The most recent trends demonstrate relatively small increases in AAA mortality across the majority of EU15+ countries since 2012.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".