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Record W2045784410 · doi:10.3109/02699052.2012.698359

Virtual reality as a screening tool for sports concussion in adolescents

2012· article· en· W2045784410 on OpenAlexaff
Pierre Nolin, Annie Stipanicic, Mylène Henry, Christian C. Joyal, Philippe Allain

Bibliographic record

VenueBrain Injury · 2012
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsConcussionVirtual realityPsychologyInjury preventionPhysical medicine and rehabilitationPoison controlMedicinePhysical therapyComputer scienceMedical emergencyHuman–computer interaction

Abstract

fetched live from OpenAlex

PRIMARY OBJECTIVE: There is controversy surrounding the cognitive effects of sports concussion. This study aimed to verify whether the technique of virtual reality could aid in the identification of attention and inhibition deficits in adolescents. STUDY DESIGN: A prospective design was used to assess 25 sports-concussed and 25 non-sports-concussed adolescents enrolled in a sport and education programme. METHODS AND PROCEDURES: Participants were evaluated in immersive virtual reality via ClinicaVR: Classroom-CPT and in real life via the traditional VIGIL-CPT. MAIN OUTCOMES AND RESULTS: The neuropsychological assessment using virtual reality showed greater sensitivity to the subtle effects of sports concussion compared to the traditional test, which showed no difference between groups. The results also demonstrated that the sports concussion group reported more symptoms of cybersickness and more intense cybersickness than the control group. CONCLUSIONS: Sports concussion was associated with subtle deficits in attention and inhibition. However, further studies are needed to support these results.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.080
GPT teacher head0.386
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2012
Admission routes1
Has abstractyes

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