7.27 Sport experience and age account for visuomotor performance more than multiple concussion history and sex
Bibliographic record
Abstract
Objective Our previous work has consistently shown a decline in cognitive-motor integration (CMI) in those with a history of concussion, those with less sport experience, and older individuals. Here we characterize CMI performance of individuals as a function of these factors. Based on rodent models, we hypothesized that those with multiple concussions would experience significantly greater neuropathological effects on the brain networks required for visuomotor performance. We predicted that performance variance (reflecting brain network function) would be accounted for by concussion group (one versus >1), after accounting for age and experience. Sex was an exploratory factor. Design Retrospective. Setting Community, University. Participants 223 asymptomatic individuals 9–53 years old (29.2% female, mean 18.9±7.0) with a history of 1–10 concussions (mean 1.7±1.4). Interventions (or Assessment of Risk Factors) Two eye-hand coordination tasks: Standard task-direct interaction with visual targets, CMI task-hand displaced from viewed targets, visual feedback reversal. Outcome Measures Six planned kinematic variables (RT, MT, variable error, constant error, peak velocity, path length). Analysis: Linear regression of experience, age, concussion number, and sex on visuomotor performance. Main Results In contrast to our hypothesis, a significant percentage of the variance was explained only by age and sport experience (p<0.05) in our sample of younger, mainly select-level athletes. Conclusions We suggest that motor developmental stage and skill experience provide brain network resilience that can compensate for concussion-related performance declines. These data emphasize the clinical importance of accounting for such factors when assessing the effects of multiple concussion on complex visuomotor skill. Future work will look at older and retired athletes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.013 | 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".