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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".