One-time screening for abdominal aortic aneurysm in Ontario, Canada: a model-based cost-utility analysis
Bibliographic record
Abstract
BACKGROUND: Screening programs for abdominal aortic aneurysm (AAA) are not available in Canada. We sought to determine the effectiveness and costutility of AAA screening in Ontario. METHODS: We compared one-time ultrasonography-based AAA screening for people aged 65 years to no screening using a fully probabilistic Markov model with a lifetime horizon. We estimated life-years, quality-adjusted life-years (QALYs), AAA-related deaths, number needed to screen to prevent 1 AAA-related death and costs (in Canadian dollars) from the perspective of the Ontario Ministry of Health. We retrieved model inputs from literature, Statistics Canada, and the Ontario Case Costing Initiative. RESULTS: Screening reduced AAA-related deaths by 84.9% among males and 81.0% among females. Compared with no screening, screening resulted in 0.04 (18.96 v. 18.92) additional life-years and 0.04 (14.95 v. 14.91) additional QALYs at an incremental cost of $80 per person among males. Among females, screening resulted in 0.02 (21.25 v. 21.23) additional life-years and 0.01 (16.20 v. 16.19) additional QALYs at an incremental cost of $11 per person. At a willingness-to-pay of $50 000 per year, screening was cost-effective in 84% (males) and 90% (females) of model iterations. Screening was increasingly cost-effective with higher AAA prevalence. INTERPRETATION: Screening for AAA among people aged 65 years in Ontario was associated with fewer AAA-related deaths and favourable cost-effectiveness. To maximize QALY gains per dollar spent and AAA-related deaths prevented, AAA screening programs should be designed to ensure that populations with high prevalence of AAA participate.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".