The interventional radiologist as "clinician": what does it mean? CanMEDS for the interventional radiologist.
Bibliographic record
Abstract
There is a large movement within the field of interventional radiology (IR) and various related organizations (including the Society of Interventional Radiology and the Canadian Interventional Radiology Association) for interventional radiologists to become more clinical. This is seen as the necessary next step for the specialty as it continues to evolve and grow. Here, we take a novel approach to the implications of becoming a full-fledged clinician. We do this in the context of the Canadian Medical Education Directions for Specialists (CanMEDS) framework, developed in the early 1990s with the support of the Royal College of Physicians and Surgeons of Canada (RCPSC) in response to changing dynamics within health care and medical education. The CanMEDS framework defines the 7 domains of competency or roles of the specialist physician: professional, manager, advocate, collaborator, scholar, communicator, and medical expert. We suggest that these roles can be adapted to define the basis of the clinician interventional radiologist, and we believe they may be employed to teach current and future IR trainees how to fill what is hoped to be their future role. We provide a brief history of the CanMEDS project, define the 7 domains of competencies, and summarize how they apply to the clinician interventional radiologist.
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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 | 0.018 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".