8.1 The effect of concussion history on an on-field assessment of cognitive-motor integration (Hurtshynes™ test) in varsity athletes
Bibliographic record
Abstract
Objective Develop an on-field cognitive-motor integration task for assessing post-concussion return readiness. Hypothesis: athletes without a history (Hx) of concussion perform faster and incur fewer errors in the different task conditions versus athletes with a Hx. Design Prospective study. Setting Canadian university. Participants 180 asymptomatic university athletes from 14 teams; 77 athletes (48 females, 29 males) with a prior Hx of concussions & 103 athletes (51 females, 52 males) without. Interventions (or Assessment of Risk Factors) Athletes ran 26m while responding to four visual directional cues from an examiner (directing athletes to jump, drop, cut right, cut left) under two different conditions. Athletes were randomly assigned to complete either a simple condition 1: athlete response direction matched the visual cue direction, or a more cognitively demanding condition 2: response direction was opposite to the visual cue. Outcome Measures Dependent variables: number of incorrect responses and total time for completion of each condition. A visuomotor learning factor was calculated based on the order that the tasks were assigned and a cognitive load factor was calculated based on the task condition. Main Results Learning effect (p=0.03): Athletes with a Hx had reduced time savings benefit from having done the basic task already. Sex/Concussion-History interaction effect (p=0.003): Males with a Hx performed on average 669ms slower on overall task performance while females with a Hx were on average 303ms faster, suggesting a better visuomotor motor skill recovery in females. Conclusions The HurtSHynes test is a useful, fast assessment of an athlete’s multi-domain skill performance to assist in guiding RTS decisions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".