The Canadian Medical Student Ultrasound Curriculum
Bibliographic record
Abstract
OBJECTIVES: This study sought to establish by expert review a consensus-based, focused ultrasound curriculum, consisting of a foundational set of focused ultrasound skills that all Canadian medical students would be expected to attain at the end of the medical school program. METHODS: An expert panel of 21 point-of-care ultrasound and educational leaders representing 15 of 17 (88%) Canadian medical schools was formed and participated in a modified Delphi consensus method. Experts anonymously rated 195 curricular elements on their appropriateness to include in a medical school curriculum using a 5-point Likert scale. The group defined consensus as 70% or more experts agreeing to include or exclude an element. We determined a priori that no more than 3 rounds of voting would be performed. RESULTS: Of the 195 curricular elements considered in the first round of voting, the group reached consensus to include 78 and exclude 24. In the second round, consensus was reached to include 4 and exclude 63 elements. In our final round, with 1 additional item added to the survey, the group reached consensus to include an additional 3 and exclude 8 elements. A total of 85 curricular elements reached consensus to be included, with 95 to be excluded. Sixteen elements did not reach consensus to be included or excluded. CONCLUSIONS: By expert opinion-based consensus, the Canadian Ultrasound Consensus for Undergraduate Medical Education Group recommends that 85 curricular elements be considered for inclusion for teaching in the Canadian medical school focused ultrasound curricula.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".