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Record W2360079264 · doi:10.2217/cnc-2016-0001

Prolonged cognitive–motor impairments in children and adolescents with a history of concussion

2016· article· en· W2360079264 on OpenAlexaff
Marc Dalecki, David Albines, Alison Macpherson, Lauren E. Sergio

Bibliographic record

VenueConcussion · 2016
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsYork University
Fundersnot available
KeywordsConcussionCognitionPsychologyPhysical medicine and rehabilitationAudiologyAsymptomaticMedicineDevelopmental psychologyPoison controlInjury preventionNeuroscienceInternal medicineMedical emergency

Abstract

fetched live from OpenAlex

AIM: We investigated whether children and adolescents with concussion history show cognitive-motor integration (CMI) deficits. METHOD: Asymptomatic children and adolescents with concussion history (n = 50; mean 12.84 years) and no history (n = 49; mean: 11.63 years) slid a cursor to targets using their finger on a dual-touch-screen laptop; target location and motor action were not aligned in the CMI task. RESULTS: Children and adolescents with concussion history showed prolonged CMI deficits, in that their performance did not match that of no history controls until nearly 2 years postevent. CONCLUSION: These CMI deficits may be due to disruptions in fronto-parietal networks, contributing to an increased vulnerability to further injury. Current return-to-play assessments that do not test CMI may not fully capture functional abilities postconcussion.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.077
Threshold uncertainty score0.268

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.021
GPT teacher head0.284
Teacher spread0.263 · 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

Citations53
Published2016
Admission routes1
Has abstractyes

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