Screening for abdominal aortic aneurysms in men: a Canadian perspective using Monte Carlo-based estimates.
Bibliographic record
Abstract
OBJECTIVE: Recently generated randomized screening trial data have provided good evidence in favour of routine screening for abdominal aortic aneurysm (AAA) to reduce AAA-related deaths in men aged 65 years and older. We developed an economic model that assessed the incremental cost-utility of AAA screening to help decision makers judge the relevance of a national screening program in Canada. METHODS: We constructed a 14 health state Markov model comparing 2 cohorts of 65-year-old men, where the first cohort was invited to attend screening for AAA using ultrasonography (US) and the second cohort followed the current practice of opportunistic detection. Lifetime outcomes included the life-years gained, AAA rupture avoided, AAA-related mortality, quality-adjusted life years (QALYs) and costs. Transition probabilities were derived from a systematic review of the literature, and a probabilistic sensitivity analysis was carried out to examine the effect of joint uncertainty in the variables of our analysis. The perspective adopted was that of the health care provider. RESULTS: Invitations to attend screening produced an undiscounted gain in life expectancy of 0.049 years and a gain in discounted QALY of 0.019 for an estimated incremental lifetime cost of CAN$118. The estimated incremental cost-utility ratio was CAN$6194 per QALY gained (95% confidence interval [CI] 1892-10 837). The numbers needed to invite to attend screening, and the numbers needed to screen to prevent 1 AAA-related death were 187 (95% CI 130-292) and 137 (95% CI 85-213), respectively. The acceptability curve showed a greater than 95% probability of the program's being cost-effective, and the model was robust to changes in the values of key parameters within plausible ranges. CONCLUSION: Our results support the economic viability of a national screening program for men reaching 65 years of age in Canada. More clinical studies are needed to define the role of screening in subgroups at high risk, especially in the female population.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".