Orthopaedic education in the era of surgical simulation: Still at the crawling stage
Bibliographic record
Abstract
Surgical skills education is in the process of a crucial transformation from a master-apprenticeship model to simulation-based training. Orthopaedic surgery is one of the surgical specialties where simulation-based skills training needs to be integrated into the curriculum efficiently and urgently. The reason for this strong and pressing need is that orthopaedic surgery covers broad human anatomy and pathologies and requires learning enormously diverse surgical procedures including basic and advanced skills. Although the need for a simulation-based curriculum in orthopaedic surgery is clear, several obstacles need to be overcome for a smooth transformation. The main issues to be addressed can be summarized as defining the skills and procedures so that simulation-based training will be most effective; choosing the right time period during the course of orthopaedic training for exposure to simulators; the right amount of such exposure; using objective, valid and reliable metrics to measure the impact of simulation-based training on the development and progress of surgical skills; and standardization of the simulation-based curriculum nationwide and internationally. In the new era of surgical education, successful integration of simulation-based surgical skills training into the orthopaedic curriculum will depend on efficacious solutions to these obstacles in moving forward.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".