Robotic Assessment of Motor, Sensory, and Cognitive Function in Acute Sport-Related Concussion and Recovery
Bibliographic record
Abstract
There is a need for better tools to objectively, reliably, and precisely assess neurological function after sport-related concussion (SRC). The aim of this study was to use a robotic device (Kinesiological Instrument for Normal and Altered Reaching Movements; KINARM) to quantify neurological impairments in athletes acutely and when clinically asymptomatic post-SRC. Robotic assessments included five KINARM standard tasks that evaluate aspects of motor, sensory, and cognitive function. We hypothesized that acutely concussed athletes would demonstrate significant rates of impairment on the robotic assessment, and that impairments would be associated with acute symptom severity. Pre-season assessments were conducted from 2011 to 2016 on 1051 athletes. Eighty-four athletes were reassessed acutely (≤10 days post-injury) and while symptomatic post-SRC and 89 when clinically asymptomatic. Forty-four parameters were measured from the KINARM assessment to characterize neurological function. Reliable change indices (80% confidence interval) identified impairments in healthy and concussed individuals for each parameter. In concussed individuals, impairment rate varied across parameters from 4% to 27% at the acute time point and from 2% to 18% when clinically asymptomatic. Healthy athlete impairment rates were between 2% and 16% across all testing time points. We identified relationships between acute symptom severity and task performance for only two parameters, both of which evaluated attributes of motor function. Overall, the KINARM identified impairments in motor, sensory, and cognitive function in athletes with SRC; however, impairment rates were low and largely did not relate to symptom severity. More complex tasks may be necessary to identify potentially subtle neurological impairments post-SRC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".