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Record W2519255342 · doi:10.2217/cnc-2016-0006

The effect of concussion history on cognitive-motor integration in elite hockey players

2016· article· en· W2519255342 on OpenAlexaff
Johanna Hurtubise, Diana J. Gorbet, Yehyah Hamandi, Alison Macpherson, Lauren E. Sergio

Bibliographic record

VenueConcussion · 2016
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsYork University
Fundersnot available
KeywordsConcussionAthletesEliteLeagueCognitionPsychologyElite athletesPhysical medicine and rehabilitationPhysical therapyInjury preventionPoison controlMedicinePsychiatryMedical emergencyPolitical science

Abstract

fetched live from OpenAlex

AIM: To observe the effects of concussion history on cognitive-motor integration in elite-level athletes. METHODS: The study included 102 National Hockey League draft prospects (n = 51 concussion history [CH]; n = 51 no history [NC]). Participants completed two computer-based visuomotor tasks, one involved 'standard' visuomotor mapping and one involved 'nonstandard' mapping in which vision and action were decoupled. RESULTS: We observed a significant effect of group on reaction time (CH slower) and accuracy (CH worse), but a group by condition interaction only for reaction time (p < 0.05). There were no other deficits found. We discussed these findings in comparison to our previous work with non-elite athletes. CONCLUSION: Previously concussed elite-level athletes may have lingering neurological deficits that are not detected using standard clinical assessments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.329
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), 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

Citations35
Published2016
Admission routes1
Has abstractyes

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