Financial Impact of PEVAR Compared with Standard Endovascular Repair in Canadian Hospitals
Bibliographic record
Abstract
OBJECTIVES: The percutaneous endovascular abdominal aortic repair (PEVAR) approach is a minimally invasive technique that has demonstrated clinical benefit over traditional surgical cut down associated with standard endovascular abdominal aortic aneurysm (AAA) repair (EVAR). The objective of our study was to evaluate the budget impact to a Canadian hospital of changing the technique for AAA repair from the EVAR approach to the PEVAR approach. METHODS: We examined the budget impact of replacing the EVAR approach with the PEVAR approach in a Canadian hospital that performs 100 endovascular AAA repairs annually. The model incorporates the costs associated with surgery, length of stay, and postoperative complications occurring within 30 days. RESULTS: The use of PEVAR in AAA repair is associated with increased access device costs when compared with the EVAR approach (CAD$1000 vs CAD$400). However, AAA repair completed with the PEVAR approach demonstrates reduced operating time (101 minutes vs 133 minutes), length of stay (2.2 days vs 3.5 days), time in the recovery room (174 minutes vs 193 minutes), and postoperative complications (6% vs 30%), which offset the increased device costs. The model establishes that switching to the PEVAR approach in a Canadian hospital performing 100 AAA repairs annually would result in a potential cost avoidance of CAD$245,120. CONCLUSIONS: A change in AAA repair technique from EVAR to PEVAR can be a cost-effective solution for Canadian hospitals.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".