A - 51 Examining the Role of Cognition in Lower Extremity Musculoskeletal Injury and Concussion Risk in Elite Athletes
Bibliographic record
Abstract
Abstract Purpose Recent evidence suggests a relationship between cognitive performance and the risk of lower extremity musculoskeletal (LEMSK) injury, and that concussion may also increase the risk of subsequent LEMSK injury, though these relationships have been largely examined in isolation. This study examined associations between pre-season cognitive performance and the risk of subsequent LEMSK injury and concussion among elite athletes. Method An observational study of elite/international-level athletes from a Canadian national sport institute. 146 athletes (female = 87) across six different sports completed pre-season cognitive testing (Vienna Test System) between 2018 and 2023. Subsequent LEMSK injuries and concussions during the year following pre-season cognitive testing were documented via an injury surveillance program. Results Pre-season cognitive performance was similar in athletes with (n = 24) and without (n = 122) a subsequent concussion, and those with (n = 100) and without (n = 46) a subsequent LEMSK (ps > 0.05). Athletes with both a subsequent LEMSK and concussion had slower baseline reaction time on a task of stress reactivity (n = 16; median RT = 0.74 s) compared to athletes with only a subsequent LEMSK (n = 84; median RT = 0.69 s; V = 900.5, p = 0.019). Concussion history was a significant predictor of both subsequent LEMSK (B = 0.94, p = 0.017) and the number of subsequent LEMSK (B = 1.23, p < 0.01). Faster RTs on inhibition tests were predictors of subsequent number of LEMSK, while controlling for concussion history (B = -10.21, p = 0.045; B = -10.60, p = 0.017). Conclusions Identifying risk factors for LEMSK injuries and concussions is critical for preventing future injuries. These preliminary results suggest a relationship between baseline reaction time performance and combined subsequent LEMSK and concussion injury.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".