Sport experience and age account for visuomotor performance more than multiple concussion history and sex
Bibliographic record
Abstract
Aims Previous studies have consistently shown a decline in cognitive-motor integration (CMI) performance in those either with a history of concussion, less sport experience or of older age. The present study sought to characterize CMI performance of individuals as a function of these factors combined. Hypothesis: relative to those with one concussion, those with multiple concussions would experience significantly greater neuropathological effects on the brain networks required for standard and rule-based visuomotor performance, resulting in impaired motor performance. Study design: Individual cross-sectional study. Level of evidence: Level 3. Materials & Methods Two hundred and twenty-three asymptomatic individuals with a concussion history participated in this study. They performed two touchscreen-based eye–hand coordination tasks, including a standard direct interaction task and one which involved CMI; target location and motor action were dissociated in the CMI task. Results A significant percentage of standard and CMI variance was explained only by age and sport experience in our sample of younger, mainly select-level athletes. Conclusion These findings may suggest that motor developmental stage, which corresponds to age, and sport experience provide brain network resilience that can compensate for concussion-related performance declines. Clinical relevance: These data provide evidence around the importance of accounting for sport experience and developmental age when evaluating return to play metrics in youth and young adults.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".