Preclerkship Point-of-Care Ultrasound: Image Acquisition and Clinical Transferability
Bibliographic record
Abstract
INTRODUCTION: The integration of point-of-care ultrasound (POCUS) in preclerkship medical education is currently popular and based on the notion that POCUS may improve diagnostic and procedural skills in medical students. However, empirical evidence demonstrating that POCUS can enhance clinical skills in preclerkship students has been lacking. We sought to evaluate anatomical sonographic knowledge and ultrasound generation capabilities associated with the implementation of a 3-h echocardiography training camp led by 2 emergency physicians and using a flipped classroom design. METHODS: Preclerkship students from the University of Ottawa (n = 32) were recruited to participate. A flipped classroom model was adopted, providing students with a 3-chaptered peer-designed, expert validated ultrasound manual before the workshop, to maximize scanning times (2 h of reading). A pretest Likert-type design was used to assess student perception of the ultrasound tool. Similarly, a pretest/post-test model was used to assess sonographic anatomical identification. In addition, a subsequent Objective Structured Clinical Examination (OSCE) test was done 3 weeks after the hands-on session, to evaluate image generation (4 cardiac views: parasternal long, parasternal short, subxiphoid, and apical 4 chambers), understanding of knobology and structural labeling. RESULTS: < .001) between pretest (average = 12.12) and post-test (average = 18.85). The OSCE, which also ascertained knowledge retention, found that 81% of students were able to generate all 4 cardiac views perfectly, 6% were able to obtain 3 views, 10% obtained 2 views and 3% successfully generated a single view. The most challenging scan to generate was the apical 4-chamber view. CONCLUSION: The positive outcomes stemming from this study reinforces the notion that formal curricular integration of POCUS at the preclerkship level has tangible benefits for medical students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".