Test-retest reliability of the kinarm end-point robot for assessment of sensory, motor and neurocognitive function in athletes
Bibliographic record
Abstract
Objective To determine the reliability of the KINARM robot in assessing sensorimotor and neurocognitive function in athletes. Design Test-retest reliability. Setting Sport Medicine Clinic. Participants Thirty-nine randomly selected athletes (mean age: 18±3 years) over 2011–2012 and 2014–2015 seasons. Outcome measures Sixty-three parameters from five robotic tasks (Visually Guided Reaching, Position Matching, Object Hit, Object Hit and Avoid, and Trail Making B) characterising sensorimotor and neurocognitive function. Each athlete was tested three times, with an average of 43±27 days between baseline (BL) and first retest (R1), and 306±32 days between R1 and second retest (R2). Reliability was assessed by intra-class correlation coefficients (ICCs) between BL and R1, and R1 and R2. Change in task performance between assessments was also evaluated with multivariate analysis of variance (MANOVA) and post-hoc comparisons within specific task parameters. Main results ICCs ranged from −0.03 to 0.94 and −0.52 to 0.93 for BL to R1 and R1 to R2, respectively. ICCs were moderate to high (≥0.5) for 46 of 63 parameters (68%) from BL to R1, and 48 of 63 parameters (76%) from R1 to R2. MANOVAs and post-hoc comparisons indicated improvements in performance on 12/63 parameters (19%) between BL and R1, and changes in performance on 11/63 parameters (17%) between BL and R2. No significant changes were revealed in parameter performance between R1 and R2 sessions. Conclusions Overall, results suggest moderate to good test-retest reliability for the majority of KINARM robot task parameters in measuring baseline sensorimotor and neurocognitive function in athletes. Competing interests Brian W Benson In the future may receive a small royalty from BKIN Technologies Ltd. in consideration for assisting with development and validation of the KINARM end-point robotic device for use in acute sport concussion assessment and management. None. Stephen H Scott Co-founder and CSO of BKIN Technologies that commercialises the KINARM robot.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".