Expert learning in musculoskeletal ultrasound – an international observational study
Bibliographic record
Abstract
To examine the effect of discovery learning on musculoskeletal ultrasound (MSUS) performance and to explore how expert learners engage in a collaborative learning environment.Experts in MSUS participated in a discovery learning session where they were divided into groups. Each participant had one attempt to solve the same MSUS case and could seek assistance from other group members or learning resources. The video-recorded sessions were analyzed using both quantitative and qualitative methods. Performance was assessed using the validated Objective Structured Assessment of Ultrasound Skills (OSAUS) tool (1-5 points per item), and an outcome score was calculated based on the number of correct MSUS images (0-4). Participants' comfort and perception of discovery learning were evaluated using a 5-point Likert scale questionnaire.28 MSUS experts from 13 different countries completed the study. The mean OSAUS score (standard deviation) was 3.96 (0.5), and the mean outcome score was 1.89 (0.9). Using Pearson's correlation coefficient, we found a significant correlation between the OSAUS score and the outcome score (0.72, p < .001). The qualitative analysis revealed that the experts used actions associated with adaptive expertise and that social hierarchy persisted in the collaborative learning environment. Finally, we found high comfort with and acceptance of the discovery learning approach.Discovery learning may be an effective teaching strategy for future advanced MSUS courses, including international Teach-the-Teachers courses. Since social hierarchy was present, a facilitator is necessary during collaborative training.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".