Building a Point of Care Ultrasound (POCUS) Curriculum in Undergraduate Medical Education Through Stepwise Development and Assessment
Bibliographic record
Abstract
Background: Point of care ultrasound (POCUS) training is increasingly incorporated in undergraduate medical education (UME). However, limited resources and lack of standard guidelines lead to questions regarding the most effective curriculum and assessment method. The authors aimed to develop a longitudinal UME POCUS curriculum through staged intervention. Year 1, which involved simulation alone, led to improved confidence without adequate knowledge. The authors hypothesized that the addition of resident-led workshops alongside faculty-led lectures would improve POCUS knowledge and confidence among third-year medical students. Methods: A prospective cohort study of third-year students on the Internal Medicine (IM) clerkship at a large academic medical center was performed, assessing efficacy of stepwise POCUS curriculum development. Previously implemented year 1 involved comparing the control cohort receiving baseline POCUS education on rounds with the experimental cohort that had access to a high-fidelity POCUS simulator. The year 2 cohort added hands-on resident-led POCUS workshops. The year 3 cohort added faculty-led lectures. All cohorts completed pre- and post-intervention confidence and knowledge-based examinations. The year 1 control cohort served as a control for the current study. Results: A total of 69 and 102 students completed both pre-/post-tests among year 2 and 3 cohorts, respectively. Both cohorts demonstrated statistically significant improvement in POCUS knowledge and confidence, with greater magnitude of improvement in year 3 with overall knowledge improving from 49.9% to 66.7% on pre- to post-intervention examination (p<0.0001). Conclusion: While simulation alone was insufficient to instill knowledge, the addition of resident-led workshops and faculty-led lectures demonstrated benefits in POCUS knowledge and confidence among medical students and represents a sustainable model of training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".