Musculoskeletal Ultrasound in Canadian Rheumatology Training Programs: Towards a National Curriculum
Bibliographic record
Abstract
Introduction: In 2019 the Royal College of Physicians and Surgeons of Canada added educational experiences in MSUS to the core competencies in rheumatology as an optional training experience. Many Canadian rheumatology programs offer MSUS training for residents, but there is currently no national ultrasound curriculum in Canada outside of externally available courses. Objectives: This thesis had two objectives. Objective one was to explore how educators prioritize competencies while developing educational content for a rheumatology MSUS curriculum. Objective two was to define the expert consensus recommendations for MSUS in Canadian post-graduate rheumatology training programs. Methods: To address objective one, we invited educators with rheumatology MSUS expertise to participate in a modified nominal group technique (NGT) with a sequential mixed methods design. For objective two, we assembled a MSUS working group including educators with rheumatology MSUS expertise, rheumatology residents, and rheumatology program directors. We used a three-stage consensus design including a modified NGT, modified Delphi technique, and structured online focus group to establish consensus among the MSUS working group on MSUS competencies that should be included in a national rheumatology resident curriculum. Results: We identified seven themes that represent key elements educators consider when prioritizing competencies during the curriculum development process, which balance two key factors: clinical utility and learnability. We used these themes to develop a conceptual framework that can be used to help guide educators when curricular content must be prioritized. For the consensus recommendations, key rheumatology MSUS stakeholders agreed that it should be mandatory for all Canadian post-graduate rheumatology trainees to learn basic ultrasound skills; how to perform a focused MSUS exam of the hands, wrists, and feet for features of inflammatory arthritis; and perform a limited MSUS exam of the knee and ankle to identify a joint effusion. Conclusion: This thesis used a hybrid of consensus methods to advance MSUS education in Canadian post-graduate rheumatology training programs. We hope that this work can contribute to the goal of a national MSUS curriculum for all Canadian post-graduate rheumatology trainees.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".