Analyzing the Influence of Video Game and Music Engagement on Technical Skills Acquisition in Dental Students in Preclinical Phase: Protocol for a Prospective, Controlled, Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: The practice of dental surgery requires a few different skills, including mental rotation of an object, precision of movement with good hand-eye coordination, and speed of technical movement. Learning these different skills begins during the preclinical phase of dental student training. Moreover, playing a musical instrument or video game seems to promote the early development of these skills. However, we found that studies specifically addressing this issue in the field of dental education are lacking. OBJECTIVE: The main aims of this study are to evaluate whether the ability to mentally represent a volume in 3D, the precision of gestures with their right and left hand, or the speed of gesture execution is better at baseline or progresses faster for players (video games or music or both). METHODS: A prospective monocentric controlled and longitudinal study will be conducted from September 2023 and will last until April 2025 in the Faculty of Dental Surgery of Nantes. Participants were students before starting their preclinical training. Different tests will be used such as Vandenberg and Kuse's mental rotation test, the modified Precision Manual Dexterity (PMD), and performing a pulpotomy on a permanent tooth. This protocol was approved by the Ethics, Deontology, and Scientific Integrity Committee of Nantes University (institutional review board approval number IORG0011023). RESULTS: A total of 86 second-year dental surgery students were enrolled to participate in the study in September 2023. They will take part in 4 iterations of the study, the last of which will take place in April 2025. CONCLUSIONS: Playing video games or a musical instrument or both could be a potential tool for initiating or facilitating the learning of certain technical skills in dental surgery. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55738.
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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 | 0.021 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".