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Record W2036491417 · doi:10.1080/13854046.2013.866272

1 year test–retest reliability of ImPACT in professional ice hockey players

2013· article· en· W2036491417 on OpenAlexaff
Jared M. Bruce, Ruben J. Echemendía, Willem Meeuwisse, Paul Comper, Amber Sisco

Bibliographic record

VenueThe Clinical Neuropsychologist · 2013
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of TorontoUniversity of Calgary
Fundersnot available
KeywordsConcussionIce hockeyIntraclass correlationPsychologyTest (biology)Reliability (semiconductor)NeurocognitiveVerbal memoryPhysical medicine and rehabilitationPhysical therapyAudiologyCognitionPoison controlMedicineClinical psychologyInjury preventionPsychometricsPsychiatry

Abstract

fetched live from OpenAlex

The Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) battery is widely used to assess neurocognitive outcomes following sports-related concussion. The purpose of this study was to examine the 1 year test-retest reliability of ImPACT in a multilingual sample of professional hockey players. A total of 305 professional hockey players were tested 1 year apart using ImPACT. Reliable change confidence intervals were calculated and test-retest reliability was measured using Pearson and Intraclass correlation coefficients. Results indicated that the 1-year test-retest reliabilities for the Visual Motor and Reaction Time Composites ranged from low to high (.52 to .81). In contrast, 1-year test-retest reliabilities for the Verbal and Visual Memory Composites were low (.22 to .58). The 1-year test-retest results provided mixed support for the use of Visual Motor and Reaction Time Composites in select samples; in contrast, the Verbal and Visual Memory Composites may not be sensitive to clinical change.

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)0.004
metaresearch head score (Gemma)0.012
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 score0.008
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.156
GPT teacher head0.494
Teacher spread0.338 · 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

Citations57
Published2013
Admission routes1
Has abstractyes

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