Pilot Project: Does formal bedside training of medical students with a FAST exam increase their knowledge and comfort level with ultrasound use in a community family medicine practice setting?
Bibliographic record
Abstract
As point of care ultrasound (PoCUS) becomes increasingly popular and a standard of care in many clinical settings, the interest for integration in medical undergraduate curriculum is also growing [1]. This project aims to assess whether formal bedside Focused Abdominal Scan for Trauma (FAST) exam training of medical students increases their knowledge and comfort with the use of bedside ultrasound in a family medicine setting at Queen’s University. Third year medical students (n=18) were recruited to participate in a training session involving a 1-hour online video and 2-hour hands-on session. Knowledge based surveys were completed before and after the training. A survey was completed 4 months after the teaching session evaluating knowledge retention, comfort, and application of skills. Student knowledge of PoCUS and FAST increased and was maintained (pre-training 56%±20%, post-training 82%±10%, p<0.001). Self-evaluation of comfort performing a FAST examination (5-point Likert scale) similarly increased post-training session (pre-training 1.4±0.8, post-training 3.8±0.9, p<0.005), but decreased 4 months later (3±1.2, p<0.005). Students in this study were unanimously interested in ultrasound training and the methods used effectively increased theoretical knowledge and comfort with use. Students did not retain their comfort levels with FAST exam 4 months after the training session, nor did they have the opportunity to utilize the skills learned. Further evidence is required to identify the applicability of these results to undergraduate curriculum development.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".