Combining traditional laparoscopic box practice with video gaming: A randomized control trial
Bibliographic record
Abstract
While there is consensus regarding a positive effect of video gaming on dexterity, little is known regarding how much traditional laparoscopic practice can or should be substituted with video gaming. This study was designed to assess the effects of varying the amount of traditional practice in a lap box trainer and video gaming on performance in two fundamentals of laparoscopic surgery (FLS) core tasks. Undergraduate and medical students were recruited and randomized into one of four groups: a control group, a lap box group, a video game group, and a combined group with 50% of the time allocated to each modality. Performance in the two FLS tasks was assessed both prior to and following the 6 training sessions. Peg transfer performance significantly improved in the lap box group (168.4 ± 70.6 s vs. 332.9 ± 178.2 s, p < 0.001), video game group (176.7 ± 53.3 s vs. 300.0 ± 101.2 s, p < 0.001) and combined group (214.2 ± 86.9 s vs. 406.8 ± 239.5 s, p = 0.002) after training. Similar improvements were also observed in precision cutting performance in the lap box group (413.1 ± 138.4 s vs. 614.3 ± 211.4 s, p = 0.002), video game group (434.1 ± 150.8 s vs. 609.2 ± 233.2 s, p = 0.007) and combined group (469.2 ± 185.3 s vs. 663.8 ± 296.3 s, p = 0.020). When analyzing improvements in performance across three different training groups compared with the control group, we found that both the laparoscopic box group ( p < 0.001) and the combined group ( p < 0.001) showed better improvement in both tasks, and the video game group had significantly better outcomes in the precision cutting task ( p = 0.003). Traditional lap box training remains the most effective method for improving the performance of simulated laparoscopic surgery. Video games can be encouraged to enhance skills retention and supplement simulated practice outside of a formal training 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".