Measuring cognitive-motor integration to detect prolonged performance declines post-concussion
Bibliographic record
Abstract
Objective There is a higher risk of re-injury for athletes with concussion history when returning to play, despite being asymptomatic and cleared for activity. One possible explanation is that current return to play assessments test thinking and moving separately, but sport activities often require their concurrent processing (cognitive-motor integration, CMI). The aim of this research is to characterize CMI performance across a range of ages and skill levels following concussion. We hypothesize that there will be CMI impairment, even when cognition and motor action measured separately are deemed recovered, and that impairment level is affected by age and skill level. Methods 133 participants with concussion history (mix of youth, adolescent, university, and elite athletes) and 130 no-concussion controls (age/sex/skill matched) performed two eye-hand coordination tasks. Participants displaced a cursor from a central to peripheral targets by either sliding their finger on a vertically-oriented touchscreen or with decoupled eye-hand coordination (targets/cursor viewed on vertical screen but finger slid on second horizontal touchscreen with 180° cursor feedback rotation). Results Children, young adult, and elite athletes with concussion history all had CMI performance deficits in movement planning, timing and execution, despite being asymptomatic and returned to play. Younger and less skilled athletes were more impaired relative to older/elite performers. Conclusions Cognitive-motor integration tasks are successful in detecting performance post-concussion relative to established assessment tools that test these domains separately. We propose that testing CMI performance, a skill crucial in sport, is important to comprehensively assess function post-concussion and to prevent re-injury. Competing interests None.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".