Teaching Point-of-Care Ultrasound in Medicine
Bibliographic record
Abstract
Point-of-care ultrasound (POCUS) is an important tool for diagnosis and management across medical specialties. This scoping review consolidates POCUS education literature, examining how curricula are developed, implemented, and assessed. We identify literature gaps, explore directions for further research, and provide recommendations for curriculum development, implementation, and improvement. Methods We conducted a scoping review per the framework outlined by Arksey & O’Malley. A systematic search of the MEDLINE, EMBASE, Cochrane, ERIC, Web of Science, and Scopus databases was conducted to identify published, English language literature, on POCUS education in undergraduate or graduate medical training. Results Of 6,164 articles identified, 421 were analyzed in depth. Curricular content included diverse diagnostic and therapeutic applications, varying significantly by specialty. Teaching modalities included in-person didactics (74%), human models (58%), simulation (33%), and web-based didactics (18%). Several studies showed better outcomes for structured vs. apprenticeship curricula, hands-on teaching vs. didactic lectures, and human models vs. simulators. Web-based didactics were as effective as in-person didactics and conveyed benefits in reusability, cost, and instructor time. Dedicated electives and boot-camps were identified as effective. Few curricula assessed knowledge retention (5%), clinical decision making (3%), learner behavior (12%), or patient outcomes (6%). Conclusion Scholarly POCUS education literature is expanding. Curricular content varies and should be tailored to specialty needs. Structured curricula utilizing hands-on learning, electives, and bootcamps can enhance educational outcomes. Higher-level outcomes such as knowledge retention, clinical decision making, learner behavior, and patient outcomes, are lacking and should be a focus of further research
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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 | 0.025 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".