Feasibility and fidelity of practising surgical fixation on a virtual ulna bone
Bibliographic record
Abstract
BACKGROUND: Surgical simulators provide a safe environment to learn and practise psychomotor skills. A goal for these simulators is to achieve high levels of fidelity. The purpose of this study was to develop a reliable surgical simulator fidelity questionnaire and to assess whether a newly developed virtual haptic simulator for fixation of an ulna has comparable levels of fidelity as Sawbones. METHODS: Simulator fidelity questionnaires were developed. We performed a stratified randomized study with surgical trainees. They performed fixation of the ulna using a virtual simulator and Sawbones. They completed the fidelity questionnaires after each procedure. RESULTS: Twenty-two trainees participated in the study. The reliability of the fidelity questionnaire for each separate domain (environment, equipment, psychological) was Cronbach α greater than 0.70, except for virtual environment. The Sawbones had significantly higher levels of fidelity than the virtual simulator (p < 0.001) with a large effect size difference (Cohen d < 1.3). CONCLUSION: The newly developed fidelity questionnaire is a reliable tool that can potentially be used to determine the fidelity of other surgical simulators. Increasing the fidelity of this virtual simulator is required before its use as a training tool for surgical fixation. The virtual simulator brings with it the added benefits of repeated, independent safe use with immediate, objective feedback and the potential to alter the complexity of the skill.
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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 | 0.007 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".