Surgical simulation training in orthopedics: current insights
Bibliographic record
Abstract
BACKGROUND: While the knowledge required of residents training in orthopedic surgery continues to increase, various factors, including reductions in work hours, have resulted in decreased clinical learning opportunities. Recent work suggests residents graduate from their training programs without sufficient exposure to key procedures. In response, simulation is increasingly being incorporated into training programs to supplement clinical learning. This paper reviews the literature to explore whether skills learned in simulation-based settings results in improved clinical performance in orthopedic surgery trainees. MATERIALS AND METHODS: A scoping review of the literature was conducted to identify papers discussing simulation training in orthopedic surgery. We focused on exploring whether skills learned in simulation transferred effectively to a clinical setting. Experimental studies, systematic reviews, and narrative reviews were included. RESULTS: A total of 15 studies were included, with 11 review papers and four experimental studies. The review articles reported little evidence regarding the transfer of skills from simulation to the clinical setting, strong evidence that simulator models discriminate among different levels of experience, varied outcome measures among studies, and a need to define competent performance in both simulated and clinical settings. Furthermore, while three out of the four experimental studies demonstrated transfer between the simulated and clinical environments, methodological study design issues were identified. CONCLUSION: Our review identifies weak evidence as to whether skills learned in simulation transfer effectively to clinical practice for orthopedic surgery trainees. Given the increased reliance on simulation, there is an immediate need for comprehensive studies that focus on skill transfer, which will allow simulation to be incorporated effectively into orthopedic surgery training programs.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".