3.12 Accuracy not speed: a robotic assessment of motor adaptation in concussed and healthy participants
Bibliographic record
Abstract
Objective Compare movement kinematics and motor adaptation in participants post-concussion with the performance of healthy controls of similar age and sex. Design Cross-sectional. Setting University of Calgary, Calgary, Canada. Participants A convenience sample of 39 participants were recruited from the University of Calgary’s Acute Sport Concussion Clinic (27 males, 12 females; median age=14 years, range:12–22, median time-since-concussion=5 days, range:2–96) and 51 healthy controls from the community (29 males, 22 females; median age=14 years, range:10–23). Assessment of Risk Factors Individuals seeking post-concussion care (5th International Consensus on Sport Concussion) and healthy controls. Outcome Participants performed reaching movements while seated in a Kinarm exoskeleton robot. Virtual targets and a real-time feedback cursor were projected into the participant’s workspace using an augmented visual display. The task involved 25 baseline trials (cursor aligned to fingertip), 125 adaptation trials (30-degree counter-clockwise rotation of the cursor position), and 25 washout trials (cursor aligned to fingertip). Kinematic variables assessed participants’ baseline movements (reaction time, peak hand speeds, movement time, speed minima). The amount of adaptation was assessed across the last 25 adaptation trials. Results Wilcoxon rank sum tests compared performance between participants post-concussion and healthy controls. There were no significant differences in baseline movements (e.g., reaction time, movement time) between the groups (all Z(88)<0.59, p>0.27). However, motor adaptation was significantly higher in controls than participants post-concussion (Z(88)=-3.24, p<0.001). Conclusions Despite similar baseline movements, participants post-concussion expressed less adaptation. Further studies will examine the utility of robotic assessments as an objective tool to detect 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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".