Open versus endovascular repair of abdominal aortic aneurysm: a survey of Canadian vascular surgeons.
Bibliographic record
Abstract
OBJECTIVE: The aim of this survey was to determine Canadian vascular surgeons' experience with elective endovascular aortic repair (EVAR) and traditional open repair and their interest in participating in an expertise- based randomized controlled trial (RCT) as opposed to a conventional RCT comparing these 2 procedures. METHODS: A single-page questionnaire was developed and sent by fax, email or post to all vascular surgeons in Canada. Nonresponders were recontacted on 2 additional occasions to improve the response rate. The questionnaire had 2 sections. The first inquired about current and past practice patterns, including experience in both open and endovascular techniques. The second investigated the surgeons' belief in the value of open as opposed to endovascular repair and the value of expertise-based RCT methodology; it also canvassed their interest in participating in a future trial. Definitions of expertise in open and endovascular repair were drawn from the published literature. Criteria to determine the feasibility of conducting an expertise-based RCT were established a priori. RESULTS: The questionnaire was sent to 259 surgeons who appeared in multiple vascular surgery databases, and the overall response rate was 56% (95% confidence interval [CI] 50%-62%). The mean career experience was 406 cases (standard deviation [SD] 359) for conventional open abdominal aortic aneurysm (AAA) repair and 24 cases (SD 48) for endovascular repair. Of the responding surgeons, 51% (95% CI 41%-60%) ranked conventional open repair as "probably superior." Respondents were equally interested in participating in an RCT using either expertise-based methodology (54%, 95% CI 44%-63%) or conventional design (51%, 95% CI 41%-60%). CONCLUSION: Uncertainty exists among vascular surgeons in Canada as to the role of endovascular surgery in the repair of AAA. A national RCT comparing open with endovascular repair in the elective setting is potentially feasible with either expertise-based or conventional design. Increases in the number of surgeons who are willing to participate and have expertise in EVAR, in addition to high recruitment rates among eligible patients, will be necessary to make such a trial feasible in Canada.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".