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Record W2100627281 · doi:10.3810/psm.2010.10.1805

Brain Functions After Sports-Related Concussion: Insights From Event-Related Potentials and Functional MRI

2010· review· en· W2100627281 on OpenAlexaff
Nadia Gosselin, Rajeet Singh Saluja, Jen‐Kai Chen, Carolina Bottari, Karen M. Johnston, Alain Ptito

Bibliographic record

VenueThe Physician and Sportsmedicine · 2010
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsConcussionFunctional magnetic resonance imagingNeuropsychologyPsychologyCognitionBrain activity and meditationPhysical medicine and rehabilitationCognitive psychologyNeuroimagingNeuroscienceMedicinePoison controlElectroencephalographyInjury prevention

Abstract

fetched live from OpenAlex

The high incidence of concussions in contact sports and their impact on brain functions are a major cause for concern. To improve our understanding of brain functioning after sports-related concussion, advanced functional assessment techniques, namely event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI), have been recently used in research studies. Contrary to neuropsychological tests that measure verbal and/or motor responses, ERPs and fMRI assess the neural activities associated with cognitive/behavioral demands, and thus provide access to better comprehension of brain functioning. In fact, ERPs have excellent temporal resolution, and fMRI identifies the involved structures during a task. This article describes ERP and fMRI techniques and reviews the results obtained with these tools in sports-related concussion. Although these techniques are not yet readily available, they offer a unique clinical approach, particularly for complex cases (ie, athletes with multiple concussions, chronic symptoms) and objective measures that provide valuable information to guide management and return-to-play decision making.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.314
Teacher spread0.285 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations34
Published2010
Admission routes1
Has abstractyes

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