Team-Based Learning & Point of Care Ultrasound (POCUS) to Augment a Preclinical Cardiovascular Physiology Course
Bibliographic record
Abstract
Introduction: There has been increasing interest in point of care ultrasound (POCUS) as a learning tool in preclinical medical anatomy and physiology courses. Few interventions have used team-based learning (TBL) to teach cardiac POCUS. This study investigates a novel TBL exercise designed to integrate cardiac anatomy, physiology, and cardiac POCUS education within a first-year cardiovascular (CV) course called Team-Based Learning – Ultrasound (TBL-US). Methods: The TBL-US exercise consisted of four phases: preparation, individual and team readiness assurance, image acquisition and application, and knowledge assessment. Six second-year students were trained to facilitate the session under physician supervision. Pre- and post-session knowledge assessments were administered to determine knowledge acquisition. Pre- and post-session surveys were administered to assess attitudes, beliefs, and confidence surrounding cardiac POCUS. Final exam scores were compared between participants and non-participants of TBL-US and stratified into high- and low-performing subgroups to account for pre-TBL baseline differences in ability between the groups. Results: A total of 54 first-year medical students completed TBL-US. Students showed significant improvement on the post-knowledge assessment compared to the pre-knowledge assessment (70.5% vs. 54.9% [p< 0.001]) and scored significantly higher on the final CV exam compared to non-participants (low-performing group: 85.92% vs. 81.02% [p=0.039], high-performing group: 89.22% vs. 85.95% [p=0.038]). Between 43.3-72.7% of students reported that TBL-US increased their understanding of CV anatomy, physiology, and cardiac POCUS. Discussion: Students found TBL-US to be a valuable teaching modality and improved student knowledge of CV anatomy, physiology, and cardiac POCUS. TBL-US effectively augments the learning of cardiac anatomy and physiology during the preclinical undergraduate medical curriculum.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".