Randomized Controlled Trials: A Systematic Review of Laparoscopic Surgery and Simulation-Based Training
Bibliographic record
Abstract
INTRODUCTION: This systematic review was conducted to analyze the impact and describe simulation-based training and the acquisition of laparoscopic surgery skills during medical school and residency programs. METHODS: This systematic review focused on the published literature that used randomized controlled trials to examine the effectiveness of simulation-based training to develop laparoscopic surgery skills. Searching PubMed from the inception of the databases to May 1, 2014 and specific hand journal searches identified the studies. This current review of the literature addresses the question of whether laparoscopic simulation translates the acquisition of surgical skills to the operating room (OR). RESULTS: This systematic review of simulation-based training and laparoscopic surgery found that specific skills could be translatable to the OR. Twenty-one studies reported learning outcomes measured in five behavioral categories: economy of movement (8 studies); suturing (3 studies); performance time (13 studies); error rates (7 studies), and global rating (7 studies). CONCLUSION: Simulation-based training can lead to demonstrable benefits of surgical skills in the OR environment. This review suggests that simulation-based training is an effective way to teach laparoscopic surgery skills, increase translation of laparoscopic surgery skills to the OR, and increase patient safety; however, more research should be conducted to determine if and how simulation can become apart of surgical curriculum.
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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 | 0.025 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".