An Evidence‐Based Template to Incorporate Ultrasound in the Undergraduate Medical Curriculum
Bibliographic record
Abstract
Bedside ultrasonography, also known as Point of Care Ultrasound (POCUS) is becoming increasingly prevalent in medical practice 1 with some calling it the modern stethoscope 2 . As a result, a number of medical schools across the United States 3–5 , Canada 6–8 , Germany 9 , France 10 , Australia 11 , and the United Kingdom 12 have incorporated POCUS into their curriculums. However, none of these countries exhibit any form of standardization or general guidelines surrounding their POCUS curriculums. This can pose difficulties for instructors who are willing to include ultrasound education in their curriculum but unsure of how to approach teaching it. Of the curriculums analyzed, we found that the majority of schools that included ultrasound teaching in the pre‐clinical years incorporated it into the anatomy curriculum. However, number of students, content covered, and time allotted to POCUS education varies from school to school. Of the published data, it was found that on average students received about 6 hours of ultrasound experience over the course of the year. Moreover, the topics taught using ultrasound technology differed but the most common were cardiovascular and abdomen anatomy. In addition, the structure of the classroom varied from one program to the next depending on the size of the cohort and number of machines available. During hands‐on practice, students were divided into groups with an average of 10.45 students per group. Thus, the question of which curriculum (or combination of curricular components) is feasible and most effective arises. By conducting an analysis of the pre‐existing literature on POCUS education, we collected information regarding how medical schools have incorporated it into their own curriculums in the past. With this information, we hope to form a template or foundation to aid instructors and course developers in implementing their own POCUS curriculums. 1. Tarique, U. et al., J. Ultrasound Med. 2018; 37:1. 2. Feilchenfeld, Z. et al., Med. Educ. 2018; 52:12. 3. Rempell, J.S. et al., West J. Emerg. 2016; 17:6. 4. Rao, S. et al., J. Ultrasound Med. 2008; 27:5. 5. Hoppmann, RA. et al., Crit. Ultrasound J. 2015; 7:8. 6. Steinmetz, P. et al., J. Ultrasound Med. 2016; 35: 9. 7. Mohialdin, V. et al., FASEB J. 2018; 32. 8. Stone‐McLean, J. et al., Cureus. 2017; 9:9. 9. Teichgräber, U.K.M. et al., Med. Educ. 1996; 30:4. 10. Hammoudi, N. et al., Arch. Cardiovasc. Dis. 2013; 106:10. 11. Moscova, M. et al., Anat. Sci. Educ. 2014; 8:3. 12. Wakefield, R.J. et al., Med. Teach. 2018; 40:6.
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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 | 0.074 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".