The development of two Point of Care Ultrasound stations for Objective Structured Clinical Examinations in undergraduate medical education
Bibliographic record
Abstract
Introduction: Point-of-care ultrasound (POCUS) is a valuable clinical skill that improves clinical care but requires substantial training. Validated assessment tools provide empirical evidence regarding trainee performance while also informing program-level evaluation. We developed two POCUS-specific stations for objective structured clinical examinations (OSCEs) to assess skill acquisition and inform best practices in undergraduate medical education. Methods: A multidisciplinary group of POCUS educators identified two POCUS applications (pleural effusion and abdominal free fluid) well suited for the undergraduate level. A modified Delphi approach was used to develop POCUS-application-specific skill checklists and global rating scale. Two medical programs piloted the stations to inform reliability. Results: Across two sites, 46 and 41 students participated in the pleural effusion and abdominal free fluid stations respectively. Checklists showed high internal reliability, with Cronbach's alpha of 0.85 (95% CI 0.71-0.93) for the pleural effusion station and 0.87 (95% CI 0.74-0.95) for the abdominal free fluid station. Krippendorff's alpha, a measure of inter-rater reliability, was also equally strong at 0.85 (95% CI 0.43-0.94) and 0.83 (95% CI 0.50-0.94) respectively. Conclusion: Both POCUS OSCE stations demonstrated good internal and inter-rater reliability. Deployment of these OSCE stations at programs with integrated POCUS curricula may help refine programming and training expectations.
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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 | 0.028 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".