Performance of carotid stenting, vertebroplasty, and EVAR: how many are we doing and why are we not doing more? A survey by the Canadian Interventional Radiology Association.
Bibliographic record
Abstract
OBJECTIVE: To determine the percentage of interventional radiologists who currently perform 3 interventional procedures: carotid stenting, vertebroplasty, and endovascular aneurysm repair (EVAR) in Canada, and impediments to their future performance by other interventional radiologists. METHODS: An anonymous online survey was emailed to all members of the Canadian Interventional Radiology Association (CIRA). The survey was open for a period of 2 months. RESULTS: A total of 75 survey responses were received (of an estimated 247). Carotid stenting, vertebroplasty, and EVAR were performed at 40%, 59%, and 46% of respondents' centres respectively. Wait times, from referral to consultation, and from consultation to procedure, were both typically between 2 to 4 weeks, longer for EVAR. Of respondents currently not performing these procedures, 26%, 28%, and 16% anticipated beginning to perform carotid stenting, vertebroplasty, and EVAR, respectively, in the proceeding year from time of survey. Of respondents who wished to perform the procedure, the greatest impediments were a lack of training, lack of a referral base, and lack of support from their radiology department and (or) colleagues. CONCLUSIONS: Although carotid stenting, vertebroplasty, and EVAR were being performed at about one-half of respondent's centres, and there will likely be greater adoption of the procedures in the near future, there remain substantial impediments. The greatest impediments to additional radiologists performing these procedures were a lack of training, lack of referral base, and lack of support from their radiology department and (or) colleagues. The former impediment suggested an unmet need for additional training courses.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".