A Scoping Review of Ultrasound Teaching in Undergraduate Medical Education
Bibliographic record
Abstract
Increasingly, medical schools are integrating Point of Care Ultrasound (POCUS) into their curricula. This review investigated the available literature on how best to integrate POCUS in the teaching of medical students and the benefits of doing so. Given the heterogeneous literature that has emerged on POCUS education, a scoping review was conducted. Relevant medical databases, including PubMed, MEDLINE, PsycINFO, EMBASE and CINAHL, were searched between January 1980 and August 2016, using keywords identified by the authors. Inclusion criteria were as follows: prospective or retrospective studies, observational or intervention studies, and studies describing how medical students learn to use ultrasound. The literature search yielded 593 articles, of which 128 met the inclusion criteria. Studies that met the inclusion criteria were sub-categorised under the following headings: those that described or evaluated an ultrasound curriculum, those that employed ultrasound as a means of teaching another topic in the curriculum (i.e., anatomy, physical examination, physiology, invasive procedures), those that investigated the learning curve of ultrasound education and those that employed adjuncts or peer mentoring to teach ultrasound. The reviewed literature indicates that the integration of ultrasound in undergraduate medical education is both feasible and beneficial to medical students. This article is intended to inform medical educators aiming to integrate ultrasound into their medical school curricula.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".