Using simulation to improve the cognitive and psychomotor skills of novice students in advanced laparoscopic surgery: A meta-analysis
Bibliographic record
Abstract
Advances in simulation technologies have enhanced the ability to introduce the teaching and learning of laparoscopic surgical skills to novice students. In this meta-analysis, a total of 18 randomized controlled studies were identified that specifically looked at training novices in comparison with a control group as it pertains to knowledge retention, time to completion and suturing and knotting skills. The combined random-effect sizes (ESs) showed that novice students who trained on laparoscopic simulators have considerably developed better laparoscopic suturing and knot tying skills (d = 1.96, p < 0.01), conducted fewer errors (d = 2.13, p < 0.01), retained more knowledge (d = 1.57, p < 0.01) than their respective control groups, and were significantly faster on time to completion (d = 1.98, p < 0.01). As illustrated in corresponding Forest plots, the majority of the primary study outcomes included in this meta-analysis show statistically significant support (p < 0.05) for the use of laparoscopic simulators for novice student training on both knowledge and advanced surgical skill development (28 of 35 outcomes, 80%). The findings of this meta-analysis support strongly the use of simulators for teaching laparoscopic surgery skills to novice students in surgical residency programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".