Using Ultrasound to Enhance Medical Students' Femoral Vascular Physical Examination Skills
Bibliographic record
Abstract
OBJECTIVES: To determine whether the addition of ultrasound to traditional physical examination instruction improves junior medical students' abilities to locate the femoral pulse. METHODS: Initially, 150 second-year medical students were taught the femoral pulse examination using traditional bedside teaching on standardized patients and online didactic videos. Students were then randomized into 2 groups: group 1 received ultrasound training first and then completed the standardized examination; and group 2 performed the standardized examination first and then received ultrasound training. On the standardized patients, the femoral artery was marked with invisible ink before the sessions using ultrasound. Compared to these markers, students were then evaluated on the accuracy of femoral artery pulse palpation and the estimated location of the femoral vein. All students completed a self-assessment survey after the ultrasound sessions. RESULTS: Ultrasound training improved the students' ability to palpate the femoral pulse (P= .02). However, ultrasound did not facilitate correct estimation of the femoral vein's anatomic location (P = .09). Confidence levels in localizing the femoral artery and vein were equal between groups at baseline, and both increased after the ultrasound sessions. CONCLUSIONS: The addition of ultrasound teaching to traditional physical examination instruction enhanced medical student competency and confidence with the femoral vascular examination. However, understanding of anatomy may require emphasis on precourse didactic material, but further study 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".