Virtual Interactive Surgical Skills Classroom: Protocol for a Parallel-Group, Noninferiority, Adjudicator-Blinded, Randomized Controlled Trial (VIRTUAL)
Bibliographic record
Abstract
BACKGROUND: Traditional face-to-face training (FFT) for basic surgical skills is inaccessible and resource-intensive. Noninteractive computer-based learning is more economical but less educationally beneficial. Virtual classroom training (VCT) is a novel method that permits distanced interactive expert instruction. VCT may optimize resources and increase accessibility. OBJECTIVE: We aim to investigate whether VCT is superior to computer-based learning and noninferior to FFT in improving proficiency in basic surgical skills. METHODS: This is a protocol for a parallel-group, noninferiority, randomized controlled trial. A sample of 72 undergraduates will be recruited from 5 medical schools in London. Participants will be stratified by subjective and objective suturing experience level and allocated to 3 intervention groups at a 1:1:1 ratio. VCT will be delivered using the BARCO weConnect software, and FFT will be provided by expert instructors. Optimal student-to-teacher ratios of 12:1 for VCT and 4:1 for FFT will be maintained. The assessed task will be interrupted suturing with hand-tied knots. RESULTS: The primary outcome will be the postintervention Objective Structured Assessment of Technical Skills score, adjudicated by 2 experts blinded to the study and adjusted for baseline proficiency. The noninferiority margin (δ) will be defined using historical data. CONCLUSIONS: This study will serve as a comprehensive appraisal of the suitability of virtual basic surgical skills classroom training as an alternative to FFT. Our findings will assist the development and implementation of further resource-efficient, accessible, virtual basic surgical skills training programs during the COVID-19 pandemic and in the future. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number ISRCTN12448098; https://www.isrctn.com/ISRCTN12448098. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/28671.
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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 | 0.032 | 0.030 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.052 | 0.011 |
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".