Medical educators' perspectives of teaching physical examinations using ultrasonography at the undergraduate level
Bibliographic record
Abstract
BACKGROUND: Ultrasonography is increasingly used for teaching physical examination in medical schools. This study seeks the opinions of educators as to which physical examinations would be most enhanced by the addition of ultrasonography. We also asked when ultrasound-aided physical examination teaching could have deleterious effects if used outside its intended scope. METHODS: All of the educators from the University of Calgary Master Teacher Program were invited to complete a 22-item paper-based survey. Survey items were generated independently by two investigators, with input from an expert panel (n = 5). RESULTS: Of the 36 educators, 27 (75%) completed the survey. Examinations identified to be potentially most useful included: measuring the size of the abdominal aorta, identifying the presence/absence of ascites, identifying the presence/absence of pleural effusions, and measuring the size of the bladder. Examinations thought to be potentially most harmful included: identifying the presence/absence of intrauterine pregnancy, measuring the size of the abdominal aorta, and identifying the presence/absence of pericardial effusion. CONCLUSIONS: Examinations that are potentially the most useful may also be potentially the most harmful. When initiating an ultrasound curriculum for physical examinations, educators should weigh the risks and benefits of examinations chosen.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".