The detection and management of abdominal aortic aneurysm: a cost-effectiveness analysis.
Bibliographic record
Abstract
BACKGROUND: Abdominal aortic aneurysm (AAA) is an important cause of death in Canada, and about 80% of the deaths are due to ruptured aneurysm. METHOD: To determine the most cost-effective way of controlling AAA in terms of early detection and clinical management, a cohort analysis was undertaken beginning at age 50 years, using a multistate life-table model with parameters derived from published articles. The model was used to determine (a) the optimum size for elective surgery and (b) the optimum rate of detection of intact AAA. Cost per quality-adjusted life-year (QALY) was used to measure outcome. RESULTS: The most cost-effective diameter for repair of an intact AAA increases with age between the limits of 55 and 70 mm. The predominant size for repair is 60 mm. The most cost-effective rate at which latent AAA should be detected is 20% per year, corresponding to a screening interval of 5 years. Selective screening by sex or smoking status, or both, does not improve cost-effectiveness. CONCLUSIONS: Primary care patients aged 50 years and over should be offered abdominal ultrasonography every 5 years. Those with AAA should be kept under surveillance and offered elective surgery when the aneurysm reaches 60 mm in diameter.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".