Development and validation of a virtual teaching method for minimally invasive surgery skills: a prospective cohort study
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic led to a significant reduction in operative exposure for surgical trainees, necessitating alternative training methods to mitigate the impact on surgical education. This study sought to evaluate whether minimally invasive surgery (MIS) skills could be taught remotely using widely available technology with objective assessments of proficiency. METHODS: This was a pilot observational study with a comparative assessment of face-to-face (F2F) and virtual training of novice learners in MIS skills. Performance and objective cognitive workload parameters [Surgical Task Load Index (SURG-TLX) score, heart rate, and pupil metrics] were evaluated. The assessments were peg transfer [McGill Inanimate System for Training and Evaluation of Laparoscopic Skills (MISTELS)] and suturing [Suturing Training and Testing (SUTT)] tasks performed using box trainers. Virtual teaching was conducted by expert trainers using a web-based streaming platform. RESULTS: Technical challenges of delivering a virtual MIS skills course were addressed after a pilot course. Participants ( n =20) in the final course had similar baseline characteristics and were randomly allocated to F2F ( n =8) and virtual ( n =12) teaching groups. Participants in the online group completed the peg transfer task faster than the F2F group (11.25 min vs. 16.88 min; P =0.015). There were no significant differences in all other MISTELS and SUTT performance measures between groups. Cognitive workload parameters (SURG-TLX score, heart rate, and pupil metrics) were also similar between groups. CONCLUSION: This study has demonstrated that virtual teaching of MIS skills using a web-based streaming platform is feasible and effective, providing the foundation for low-cost, effective, and scalable MIS skills programs in the future.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".