The Effect of Computer Navigation on Trainee Learning of Surgical Skills
Bibliographic record
Abstract
BACKGROUND: While computer-assisted orthopaedic surgery technology may facilitate performance and learning in the expert, its effects on the trainee are unclear. Motor learning theory suggests that, while the real-time feedback provided by computer-assisted orthopaedic surgery should improve performance, it may be detrimental to learning. The purpose of this study was to assess the effects of computer-assisted orthopaedic surgery on the learning of surgical skills by trainees. METHODS: Forty-five participants were randomized to one of three training groups-conventional training, computer navigation, or knowledge of results-in which they learned technical skills related to total hip replacement. Outcomes were assessed in a pretest session and in ten-minute and six-week retention and transfer tests. RESULTS: All groups demonstrated improved accuracy and precision in the determination of the abduction angle and the version angle of the acetabular cups during training (p < 0.001). The computer navigation group demonstrated significantly better accuracy and precision in early training (p < 0.05) and better precision throughout training (p < 0.05). No significant degradation in performance was observed between the immediate and the delayed testing for any group, suggesting that there was task learning in all groups with no negative effects of the tested training modalities on learning. CONCLUSIONS: In this study, the concurrent augmented feedback provided by computer-assisted orthopaedic surgery resulted in improved early performance and equivalent learning. While we did not observe a compromise in learning, further investigation is required to ensure that computer-assisted orthopaedic surgery does not compromise trainee learning in more complex tasks.
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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 | 0.004 | 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".