Advancing Simulation-Based Orthopaedic Surgical Skills Training: An Analysis of the Challenges to Implementation
Bibliographic record
Abstract
Simulation-based surgical skills training is recognized as a valuable method to improve trainees' performance and broadly perceived as essential for the establishment of a comprehensive curriculum in surgical education. However, there needs to be improvement in several areas for meaningful integration of simulation into surgical education. The purpose of this focused review is to summarize the obstacles to a comprehensive integration of simulation-based surgical skills training into surgical education and board certification and suggest potential solutions for those obstacles. First and foremost, validated simulators need to be rigorously assessed to ensure their feasibility and cost-effectiveness. All simulation-based courses should include clear objectives and outcome measures (with metrics) for the skills to be practiced by trainees. Furthermore, these courses should address a wide range of issues, including assessment of trainees' problem-solving and decision-making abilities and remediation of poor performance. Finally, which simulation-based surgical skills courses will become a standard part of the curriculum across training programs and which will be of value in board certification should be precisely defined. Sufficient progress in these areas will prevent excessive development of training and assessment tools with duplicative effort and large variability in quality.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".