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Record W2799323302 · doi:10.1371/journal.pone.0196205

Test-retest reliability of the KINARM end-point robot for assessment of sensory, motor and neurocognitive function in young adult athletes

2018· article· en· W2799323302 on OpenAlexafffund
Cameron S. Mang, Tara A. Whitten, Madeline S. Cosh, Stephen H. Scott, J. Preston Wiley, Chantel T. Debert, Sean P. Dukelow, Brian W. Benson

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsQueen's UniversityHotchkiss Brain InstituteOntario Brain InstituteUniversity of Calgary
FundersMitacsCanadian Institutes of Health ResearchAlberta InnovatesAlberta Innovates - Health SolutionsCanadian Academy of Sport and Exercise Medicine
KeywordsAthletesPhysical medicine and rehabilitationReliability (semiconductor)MedicineNeurocognitivePsychologyTest (biology)Physical therapyCognitionPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Current assessment tools for sport-related concussion are limited by a reliance on subjective interpretation and patient symptom reporting. Robotic assessments may provide more objective and precise measures of neurological function than traditional clinical tests. OBJECTIVE: To determine the reliability of assessments of sensory, motor and cognitive function conducted with the KINARM end-point robotic device in young adult elite athletes. METHODS: Sixty-four randomly selected healthy, young adult elite athletes participated. Twenty-five individuals (25 M, mean age±SD, 20.2±2.1 years) participated in a within-season study, where three assessments were conducted within a single season (assessments labeled by session: S1, S2, S3). An additional 39 individuals (28M; 22.8±6.0 years) participated in a year-to-year study, where annual pre-season assessments were conducted for three consecutive seasons (assessments labeled by year: Y1, Y2, Y3). Forty-four parameters from five robotic tasks (Visually Guided Reaching, Position Matching, Object Hit, Object Hit and Avoid, and Trail Making B) and overall Task Scores describing performance on each task were quantified. RESULTS: Test-retest reliability was determined by intra-class correlation coefficients (ICCs) between the first and second, and second and third assessments. In the within-season study, ICCs were ≥0.50 for 68% of parameters between S1 and S2, 80% of parameters between S2 and S3, and for three of the five Task Scores both between S1 and S2, and S2 and S3. In the year-to-year study, ICCs were ≥0.50 for 64% of parameters between Y1 and Y2, 82% of parameters between Y2 and Y3, and for four of the five Task Scores both between Y1 and Y2, and Y2 and Y3. CONCLUSIONS: Overall, the results suggest moderate-to-good test-retest reliability for the majority of parameters measured by the KINARM robot in healthy young adult elite athletes. Future work will consider the potential use of this information for clinical assessment of concussion-related neurological deficits.

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)0.007
metaresearch head score (Gemma)0.010
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 score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.092
GPT teacher head0.328
Teacher spread0.236 · 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".

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Citations32
Published2018
Admission routes2
Has abstractyes

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