Ultrasound in undergraduate medical education: a systematic and critical review
Bibliographic record
Abstract
CONTEXT: The use of point-of-care ultrasound (POCUS) in clinical care is growing rapidly and advocates have recently proposed the integration of ultrasound into undergraduate medical education (UME). The evidentiary basis for this integration has not been evaluated critically or systematically. OBJECTIVES: We conducted a critical and systematic review framed by the rationales enumerated in academic publications by advocates of ultrasound in UME. METHODS: This research was conducted in two phases. First, the dominant discursive rationales for the integration of ultrasound in UME were identified from an archive of 403 academic publications using techniques from Foucauldian critical discourse analysis (CDA). We then sought empirical evidence in support of these rationales, using a critical synthesis methodology also adapted from CDA. RESULTS: We identified four dominant discursive rationales with different levels of evidentiary support. The use of ultrasound was not demonstrated to improve students' understanding of anatomy. The benefit of ultrasound in teaching physical examination was inconsistent and rests on minimal evidence. With POCUS, students' diagnostic accuracy was improved for certain pathologies, but findings were inconsistent for others. Finally, the rationale that ultrasound training in UME will improve the quality of patient care was difficult to evaluate. CONCLUSIONS: Our analysis has shown that the frequently repeated rationales for the integration of ultrasound in UME are not supported by a sufficient base of empirical research. The repetition of these dominant discursive rationales in academic publications legitimises them and may preclude further primary research. As the value of clinical ultrasound use by medical students remains unproven, educators must consider whether the associated financial and temporal costs are justified or whether more research is required.
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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 | 0.108 | 0.360 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.037 | 0.026 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".