Acquisition and Long‐term Retention of Bedside Ultrasound Skills in First‐Year Medical Students
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to assess bedside ultrasound skill acquisition and retention in medical students after completion of the first year of a new undergraduate bedside ultrasound curriculum at McGill University. METHODS: Skill acquisition was assessed in first-year medical students (n = 195) on completion of their bedside ultrasound instruction. Instruction included 6 clinically based 60-minute practical teaching sessions evenly spaced throughout the academic year. Students' ability to meet course objectives was measured according to a 4-point Likert rating scale. Evaluations were performed by both instructors and the students themselves. Retention of skill acquisition was evaluated 8 months later on a year-end practical examination. RESULTS: The mean percentage ± SD of students assigned a rating of "strongly agree" or "agree" by instructors was 98% ± 0.4% for all 6 teaching sessions (strongly agree, 52% ± 3%; agree, 46% ± 3%). According to student self-evaluations, the mean percentage of students assigned a rating of strongly agree was significantly greater than the percentage assigned by instructors for all teaching sessions (86% ± 2% versus 52% ± 3%; P < .0005). Evaluation of skill retention on the year-end examination showed that 91% ± 2% of students were assigned a rating of strongly agree or agree for their ability to demonstrate skills learned 8 months previously. Ninety-five percent of students reported that bedside ultrasound improved their understanding of anatomy for all 6 teaching sessions (mean, 95% ± 0.01%). CONCLUSIONS: These results demonstrate that first-year medical students show acquisition and long-term retention of basic ultrasound skills on completion of newly implemented bedside ultrasound instruction.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it