Meta-analysis of the current prevalence of screen-detected abdominal aortic aneurysm in women
Bibliographic record
Abstract
BACKGROUND: Although women represent an increasing proportion of those presenting with abdominal aortic aneurysm (AAA) rupture, the current prevalence of AAA in women is unknown. The contemporary population prevalence of screen-detected AAA in women was investigated by both age and smoking status. METHODS: A systematic review was undertaken of studies screening for AAA, including over 1000 women, aged at least 60 years, done since the year 2000. Studies were identified by searching MEDLINE, Embase and CENTRAL databases until 13 January 2016. Study quality was assessed using the Newcastle-Ottawa scoring system. RESULTS: Eight studies were identified, including only three based on population registers. The largest studies were based on self-purchase of screening. Altogether 1 537 633 women were screened. Overall AAA prevalence rates were very heterogeneous, ranging from 0·37 to 1·53 per cent: pooled prevalence 0·74 (95 per cent c.i. 0·53 to 1·03) per cent. The pooled prevalence increased with both age (more than 1 per cent for women aged over 70 years) and smoking (more than 1 per cent for ever smokers and over 2 per cent in current smokers). CONCLUSION: The current population prevalence of screen-detected AAA in older women is subject to wide demographic variation. However, in ever smokers and those over 70 years of age, the prevalence is over 1 per cent.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| 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".