Simulation in Surgery
Bibliographic record
Abstract
OBJECTIVE: To review the current state of simulation use in surgery and to offer direction for future research and implementation of evidence-based findings. BACKGROUND: Simulation-based training (SBT) in surgery has surged in recent years. Although several new simulators and curricula have become available, their optimization and implementation into surgical training has been lagging. METHODS: Members of the Association for Surgical Education Simulation Committee with expertise in surgical simulation review and interpret the literature and describe the current status of the use of simulation in surgery, identify the challenges to its widespread adoption, and offer potential solutions to these challenges. The review focuses on simulation research and implementation of existing knowledge and explores possible future directions for the field. RESULTS: Skill acquired on simulators has repeatedly and consistently been demonstrated to transfer to the operating room, and proficiency-based training maximizes this benefit. Several simulation-based curricula have been developed by national organizations to support resident training, but their implementation is lagging because of inadequate human resources, difficult integration of SBT into educational strategy, and logistical barriers. In research, lack of coordinated effort, flaws in study design, changes in simulator-validation concepts, limited attention to skill retention, and other areas are in need of improvement. CONCLUSIONS: Future research in surgical simulation should focus on demonstrating the cost-effectiveness of SBT and its impact on patient outcomes. Furthermore, to enable the more widespread incorporation of best practices and existing simulation curricula in surgery, effective implementation strategies need to be developed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".