922 MEP049 – Using robotics to assess motor learning impairments after concussion at time of physician clearance
Bibliographic record
Abstract
There remains a lack of gold standard tools available to physicians to inform clearance criteria from concussion; a heavy reliance is placed on self-reported symptom scales which have inherent limitations of reliability. Further, growing evidence is showing that the neurophysiological underpinnings of concussion can persist past clinical recovery. As such, with this study, we aimed to provide objective, sensitive measures of neurological function of youth athletes who have sustained a concussion at the time of physician clearance to return to sport (RTP). As a sub-study of the National Football League-funded pan-Canadian study SHRed Concussions, we recruited (n=99) youth athletes who suffered a clinician-diagnosed sport-related concussion, and performed robotic testing on the day of RTP. We hypothesized that motor learning impairments, as assessed by a visuomotor rotation task, would be present at time of RTP, as compared to healthy age, sex, and sport-matched controls (n=89). The RTP group showed significantly reduced final adaptation to the visuomotor rotation task as compared to the control group, suggesting motor learning impairments are persisting and present at time of physician clearance. Results suggest that clinical recovery may not be sufficient in capturing the neurophysiological recovery of concussion, and objective robotic tools may provide a basis for informing clinician decision making to prevent premature clearance to return to sport, reducing risk of reinjury after concussion.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".