Reliability of Scores Computed by a Commercial Virtual Reality System and Association with Indices of Cognitive Performance in Male Elite Rugby Players
Bibliographic record
Abstract
Purpose: To examine the reliability of scores calculated from virtual reality (VR) games and their association with inhibitory control and cognitive flexibility in young elite rugby players. Methods: Following a familiarization session, seventeen rugby union players completed a session of a modified Stroop test and two sessions of three VR games consisting of (1) memorizing moving targets (Tracker Master); (2) selecting moving targets while avoiding pitfalls (Beat Master—Never Stop); and (3) selecting moving targets with an increasing frequency of appearance (Beat Master—Turbo). Results: The reliability of Beat Master—Never Stop was poor to moderate (0.41 < intraclass coefficient correlation [ICC] < 0.62; 3.2% < standard error of measurement [SEM] < 26.1%), while it was good to very good for Beat Master—Turbo (0.77 < ICC < 0.87; 3.2% < SEM < 18.2%). Regarding Tracker Master, reliability was considered as low to moderate (0.22 < ICC < 0.60; 2.2% < SEM < 6.0%). We found strong associations between Tracker Master and Stroop flexibility scores (−0.55 < r < −0.64), as well as strong to very strong associations between Beat Master—Never Stop scores and the Stroop inhibition score (0.52 < ∣r∣ < 0.84). Conclusions: Considering their metrological properties and their association level with inhibition and flexibility, the sensibility scores of the Beat Master—Never Stop and Tracker Master games should be preferred for monitoring training load, provided at least two familiarization sessions precede them.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".