Does Smartphone Gaming have a Positive Effect on the Acquisition of Laparoscopic Skills?
Bibliographic record
Abstract
Aims: Videogaming has been shown to have a positive effect on hand-eye co-ordination, improve visuo-spatial ability and improve cognitive flexibility. However, the majority of the literature examining the subject utilise gaming consoles where physical controllers are used to dictate on- screen movements. The current evidence examining the association between laparoscopic skills and smartphone or touch-based gaming is extremely limited. This study seeks to examine whether smartphone gaming has a positive effect on laparoscopic skill acquisition. \nStudy Design: Prospective cohort study \nPlace and Duration of Study: Department of Urology, Sarawak General Hospital, Kuching Sarawak between August 2020 and January 2021. \nMethodology: We included 74 medical students (26 male, 48 female, age range 22 – 24 years) without any prior exposure to either laparoscopy or surgery. Subjects were assigned to either Gamer or Non-Gamer groups based on a self-reported questionnaire. Formal testing of laparoscopic skills was undertaken via the Modified MISTELS (McGill Inanimate System for Training and Evaluation of Laparoscopic Skills) to establish a baseline. The Gamer group were then asked to play at least 30 minutes of a smartphone game for 21 days whilst the Non-Gamer group were asked to refrain from commencing any virtual games. Repeat assessment of laparoscopic skills was performed and scores between the 2 groups was compared using 2-tailed independent t-test. \nResults: In total 74 medical students completed the study with 34 in the Non-Gamer Group and 40 in the Gamer Group. There was no statistically significant difference between groups at baseline assessment. Following the intervention period, the Gaming Group performed significantly better than the Non-Gaming Group with a mean Overall score of 261.05 vs 154.99 (p<.001). Additionally, the Gaming group showed statistically significant higher scores in all three component tasks, with most marked difference in Intracorporeal Suturing. \nConclusion: Smartphone gaming requiring the use of multi-action gestures improves the ability of novices to acquire laparoscopic skills compared to no exposure. The findings support the use of smartphone gaming to be used as an adjunct to laparoscopic training due to the convenience, portability and ease of access to most medical students and professionals.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".