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Record W4400721601 · doi:10.3390/brainsci14070715

Maintaining Cognitive Performance at the Expense of Gait Speed for Asymptomatic Concussed Athletes: A Novel Dual-Task and Post-Exercise Assessment

2024· article· en· W4400721601 on OpenAlexafffund
Gabriel Lavoie, Mathieu Bolduc, Veronik Sicard, Franco Leporé, Dave Ellemberg

Bibliographic record

VenueBrain Sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsAgricultural Research Institute of OntarioUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAthletesPhysical medicine and rehabilitationTask (project management)GaitCognitionDual (grammatical number)AsymptomaticPhysical therapyPsychologyMedicineComputer scienceEngineeringNeuroscience

Abstract

fetched live from OpenAlex

Our goal was to evaluate persisting deficits in gait and executive functioning in asymptomatic athletes with a history of concussion using a novel approach combining a dual-task paradigm and post-exercise exertion. Thirty-eight athletes aged 17 to 25 years old participated in the study, including 18 with a history of concussion. The dual-task paradigm required walking continuously at a predetermined self-paced target speed of 6.5 km/h while executing a complex switch task. Athletes completed two conditions, each on separate days: (1) dual task alone and (2) dual task following 20 min of running on a non-motorized treadmill. The statistical analyses revealed a significant reduction in gait speed exclusively for athletes with a history of concussion and only following the post-exercise condition (p = 0.008). These findings suggest that although asymptomatic concussed athletes maintain a cognitive performance comparable to non-concussed athletes, this appears to be achieved at the expense of gait speed. Our results underscore the importance of incorporating gait assessments and post-exercise exertion into concussion evaluation protocols in both research and clinical settings.

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)0.004
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.079
GPT teacher head0.390
Teacher spread0.311 · 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 designBench or experimental
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

Citations4
Published2024
Admission routes2
Has abstractyes

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