Clinical practice change requires more than comparative effectiveness evidence: abdominal aortic aneurysm management in the USA
Bibliographic record
Abstract
Adoption of healthcare innovations frequently outpaces the evidence of effectiveness. Endovascular repair (EVAR) for abdominal aortic aneurysms in the USA demonstrates how comparative effectiveness research without evidence-based reimbursement changes may fail to influence clinical practice. Randomized controlled trials for small abdominal aortic aneurysms demonstrate no lasting benefits of EVAR or open surgical repair (OSR) compared with surveillance, and for large abdominal aortic aneurysms demonstrate no lasting survival benefit of EVAR over OSR, and do show poorer durability and higher costs for EVAR. Nonetheless, >50% of elective abdominal aortic aneurysm repairs in the USA use EVAR. Factors that may be driving the high use of EVAR include patient preference, surgeons' desire to appear 'up-to-date' in the procedures they offer, higher hourly surgeon reimbursement for EVAR than OSR, and the expansion of physician specialties able to perform abdominal aortic aneurysm repair from only vascular surgeons with OSR, to vascular surgeons and interventional radiologists/cardiologists with EVAR. By comparison, in Canada, where government health insurance restricts EVAR coverage to high surgical risk patients, only approximately 25% of abdominal aortic aneurysm repairs are performed using EVAR. Country-specific cost studies and a prospective population-based study collecting detailed clinical data to identify patient subgroups that truly benefit from a particular management strategy are needed to inform policy regarding EVAR availability and reimbursement.
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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 | 0.286 | 0.588 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".