Current issues of interventional radiology in Canada: a national survey by the Canadian Interventional Radiology Association.
Bibliographic record
Abstract
OBJECTIVE: To determine current issues facing the field of interventional radiology (IR) in Canada. METHODS: An anonymous online survey was emailed to all members of the Canadian Interventional Radiology Association. The survey was open for 1 month. RESULTS: A total of 83 survey responses were received (of an estimated possible 233). Responses regarding demographics, aspects of practice, research, and IR trainee education were collected. CONCLUSIONS: Several issues were identified as pertinent to Canadian interventional radiologists, including a current and future drought of interventional radiologists, a lack of women in the profession, inadequate protected research time for those in academic practice, a lack of protected clinical time, concern regarding turf issues with other specialties, division between interventional and diagnostic radiology, and the ideal profile of the future interventional radiologist. The field of interventional radiology (IR) continues to develop, expand, and mature at a rapid pace. As the field is still relatively young, several issues are bound to arise. It is important therefore to stay abreast of the current trends and opinions of practitioners within the field.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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 | 0.001 |
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".