Remote Instruction in Focused Assessment With Sonography in Trauma (FAST) Exams for Surgery Residents: A Pilot Study
Bibliographic record
Abstract
BackgroundThe Focused Assessment with Sonography in Trauma (FAST) exam is an important component to the evaluation of trauma patients. With advances in technology and meeting limitations due to COVID-19, remote instruction and learning have gained popularity. We sought to determine whether remote instruction of FAST exams was feasible as sustainable surgical education and a possible alternative to traditional in-person teaching.MethodsGeneral surgery residents completed a baseline survey and skills assessment on FAST exams and were then randomized to remote or in-person instruction. The remote group participated in an instructional session with a content expert through video conference and then practiced on a simulated mannequin while the expert remotely provided feedback. The in-person group received the experience with the content expert in the room. Both groups completed a post-course survey immediately after the session and a follow-up survey and objective assessment at six-months. Results were compared with two-way analysis of variance (ANOVA).Results14 residents underwent the curriculum, seven in each group. There was a significant increase in self-reported confidence when comparing pre- and immediate post-course results for both the remote and in-person groups. At six months, confidence scores remained elevated and skill assessment scores improved, although the latter did not reach significance. There was no significant difference in post-course results between the groups.ConclusionsRemote instruction of FAST exams was feasible. Pilot data demonstrated an increase in confidence and suggest outcomes that are similar to in-person instruction, which has positive implications for future remote educational and potentially clinical initiatives.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".