Validation of the kinarm end-point robot for clinical assessment of acute sport concussion sensory, motor, and neurocognitive impairment in athletes
Bibliographic record
Abstract
Objective To determine if there is a significant association between KINARM robotic sensorimotor and neurocognitive measurements at baseline, ≤10 days post-concussion (IPC), and when clinically asymptomatic (CA). Design Double-blind, prospective case series. Setting Four athletic seasons (2011–2015). Participants 1,214 elite athletes (904 males, 310 females, mean age: 18 and 20 years, respectively). Outcome measures Fifty-seven parameters from five robotic tasks (Visually Guided Reaching (VGR), Position Matching (PM), Object Hit, Object Hit and Avoid, Trail Making B (TMB)) characterising sensorimotor and neurocognitive function. Linear regression was used to determine if there was a significant association between baseline, IPC, and CA measurements, adjusting for potential predictors (age, sex, concussion history, recurrent concussion during study, number of baseline assessments, method (seated versus standing), Post-Concussion Symptom Scale Score (PCSS), days post-concussion at testing) and learning effect. Main results 95 athletes sustained 102 concussions. There was a clear reduction in performance in concussed athletes on individual parameters compared to non-concussed repeat performance. Significant predictors of impairment were: 1) higher PCSS for VGR (IPC): Reaction Time (p<0.001), Min-Max Speed Difference (m/s) (p=0.001), Path Length Ratio (p=0.001), and TMB (CA): Test Time (p=0.003), Dwell Time (p=0.003); 2) older athletes for VGR (IPC): Speed Maxima Count (p<0.01); 3) less days post-concussion at IPC testing for PM Contraction/Expansion Ratio XY (p=0.005); and 4) recurrent concussion during study period for non-dominant PM Shift Y (p=0.005). Conclusions Results of this large prospective study suggest the KINARM robot is a valid, objective tool for quantifying sensorimotor and neurocognitive impairment in concussed 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. 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".