Remote Video-Delivered Suturing Education with Smartphones: A Non-Inferiority Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Objective: To measure remote feedback's educational benefit, assess its perceived feasibility and utility, and demonstrate implementation of a practical and cost-effective model. Design: Medical students were randomized to receive live video- or recorded video-delivered feedback on suturing skills. A non-randomized control group received in-person feedback. Pre- and post-feedback recordings of suturing were evaluated by blinded assessors to determine improvement using the University of Bergen suturing skills assessment tool (UBAT) and Objective Structured Assessment of Technical Skills (OSATS). Study arms were compared to the control arm in a non-inferiority analysis. Participants and feedback providers completed questionnaires regarding feasibility and utility of their feedback modality. Participants: Fifty-four first- and second-year medical student participants and 11 surgical resident feedback providers McMaster University. Results: UBAT score change was 40.5 in the remote live video feedback group, 8.7 in the remote recorded video feedback group, and 18.0 in the in-person feedback group with no significant difference between groups (p=0.619). However, 95% confidence intervals did not exclude a non-inferiority threshold for either video-based experimental arm. Similar findings were demonstrated using the OSATS tool. Questionnaire responses found that participants and feedback providers both rated video-delivered feedback as feasible and useful. Conclusions: There was no significant difference in learner improvement between live or recorded video-delivered feedback and in-person feedback, but non-inferiority was not established. We have demonstrated subjective feasibility and utility of a highly-accessible and affordable model of remote video-delivered feedback in technical skills acquisition.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".