A Comparison of Orthopaedic Resident Performance on Surgical Fixation of an Ulnar Fracture Using Virtual Reality and Synthetic Models
Bibliographic record
Abstract
BACKGROUND: Surgical trainees develop surgical skills using various techniques, with simulators providing a safe learning environment. Fracture fixation is the most common procedure in orthopaedic surgery, and residents may benefit from simulated fracture fixation. The performance of residents on a virtual simulator that allows them to practice the surgical fixation of fractures by providing a sense of touch (haptics) has not yet been compared with their performance using other methods of practicing fracture fixation, such as a Sawbones simulator model. The purpose of this study was to assess whether residents performed similarly on a newly developed virtual simulator compared with a Sawbones simulator fracture fixation model. METHODS: A stratified, randomized controlled study involving twenty-two orthopaedic surgery residents was performed. The residents were randomized to first perform surgical fixation of the ulna on either the virtual or the Sawbones simulator, after which they performed the same procedure on the other simulator. Their performance was evaluated by examiners experienced in fracture fixation who completed a task-specific checklist, global rating scale (GRS) form, and time-to-completion record for each participant on each simulator. RESULTS: Both simulators distinguished between differing experience levels, demonstrating construct validity; for the Sawbones simulator, the Cohen d value (effect size) was >0.90, and for the virtual simulator, d was >1.10 (p < 0.05 for both). The participants achieved significantly better scores on the virtual simulator compared with the Sawbones simulator (p < 0.05) for all measures except time to completion. The GRS scores showed a high level of internal consistency (Cronbach α, >0.80). However, Pearson product-moment correlation analysis showed no significant correlations between the results on the two simulators; therefore, concurrent validity was not achieved. CONCLUSIONS: The newly developed virtual ulnar surgical fixation simulator, which incorporates haptics, shows promise for helping surgical trainees learn and practice basic skills, but it did not attain the same standards as the current standard Sawbones simulator. The procedural measures used to assess resident performance demonstrated good reliability and validity, and both the Sawbones and the virtual simulator showed evidence of construct validity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".