State of the Evidence on Simulation-Based Training for Laparoscopic Surgery
Bibliographic record
Abstract
In Brief Objective: Summarize the outcomes and best practices of simulation training for laparoscopic surgery. Background: Simulation-based training for laparoscopic surgery has become a mainstay of surgical training. Much new evidence has accrued since previous reviews were published. Methods: We systematically searched the literature through May 2011 for studies evaluating simulation, in comparison with no intervention or an alternate training activity, for training health professionals in laparoscopic surgery. Outcomes were classified as satisfaction, skills (in a test setting) of time (to perform the task), process (eg, performance rating), product (eg, knot strength), and behaviors when caring for patients. We used random effects to pool effect sizes. Results: From 10,903 articles screened, we identified 219 eligible studies enrolling 7138 trainees, including 91 (42%) randomized trials. For comparisons with no intervention (n = 151 studies), pooled effect size (ES) favored simulation for outcomes of knowledge (1.18; N = 9 studies), skills time (1.13; N = 89), skills process (1.23; N = 114), skills product (1.09; N = 7), behavior time (1.15; N = 7), behavior process (1.22; N = 15), and patient effects (1.28; N = 1), all P < 0.05. When compared with nonsimulation instruction (n = 3 studies), results significantly favored simulation for outcomes of skills time (ES, 0.75) and skills process (ES, 0.54). Comparisons between different simulation interventions (n = 79 studies) clarified best practices. For example, in comparison with virtual reality, box trainers have similar effects for process skills outcomes and seem to be superior for outcomes of satisfaction and skills time. Conclusions: Simulation-based laparoscopic surgery training of health professionals has large benefits when compared with no intervention and is moderately more effective than nonsimulation instruction. We appraised the literature to summarize the outcomes and describe best practices of simulation training for laparoscopic surgery. Results show that simulation-based training in laparoscopic surgery provides large benefits when compared with no intervention and moderate benefits in comparison with nonsimulation instruction. Box trainers appear comparable with virtual reality simulators.
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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 | 0.024 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".