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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".