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Record W2789973080 · doi:10.1080/02699052.2018.1429022

Attentional dysfunction and recovery in concussion: effects on the P300m and contingent magnetic variation

2018· article· en· W2789973080 on OpenAlexafffund
Lauren Petley, Tim Bardouille, D. Chiasson, Patrick Froese, Steven Patterson, Aaron J. Newman, Antonina Omisade, Steven Beyea

Bibliographic record

VenueBrain Injury · 2018
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsDalhousie UniversityNova Scotia Health AuthorityIzaak Walton Killam Health Centre
FundersAtlantic Canada Opportunities AgencyFondation Brain Canada
KeywordsRivermead post-concussion symptoms questionnaireConcussionMagnetoencephalographyTraumatic brain injuryPsychologyPhysical medicine and rehabilitationAudiologyMedicinePhysical therapyPoison controlInjury preventionElectroencephalographyPsychiatryEmergency medicine

Abstract

fetched live from OpenAlex

PRIMARY OBJECTIVE: To examine the effect of concussion on indices of attention using magnetoencephalography. METHODS AND PROCEDURES: Thirteen patients were recruited from the emergency department and scanned within 3-6 days of injury. Five returned for follow-up scans one and three months post-injury. Thirteen healthy controls also completed testing. During MEG acquisition, participants performed the Attention Network Test (ANT). Cognitive evoked responses to this task include a cue-evoked P300m, a contingent magnetic variation (CMV) and a target-evoked P300m. The Rivermead Postconcussion Symptom Questionnaire and Sport Concussion Assessment Tool (SCAT3) were administered in all sessions. RESULTS: Patients suffering from concussion had slower response times and benefitted more from spatial cues than did controls. Global activation for all three evoked responses was lower for patients than controls. In a small sample of patients who returned for follow-up, the CMV and target P300m improved with recovery. CONCLUSIONS: MEG-evoked responses to the ANT reveal neurophysiological evidence of attentional dysfunction within days of injury. A pattern of improvement was also observed over the course of three months for the P300m, while behavioural performance did not change significantly. Further development of this method may yield a useful adjunct to neurological examination for concussion diagnosis and monitoring.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.028
GPT teacher head0.301
Teacher spread0.273 · 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

Citations10
Published2018
Admission routes2
Has abstractyes

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