Does concussion history affect softball pitch recognition, swing timing, and swing decision making in collegiate softball players?
Bibliographic record
Abstract
Concussions can affect an athlete’s cognitive and physical performance. The negative effects of concussion can linger beyond symptom resolution and can result in reduced sport performance and increased risk of injury upon return to play. The effect of concussion history, including time since concussion and number of concussions, on sport performance is not well understood. The purposes of this study were to examine the effects of concussion history on softball batting measures, such as pitch recognition, swing timing, and swing decision making, and to compare a computerized reaction time (RT) test to a sport-specific RT test. A cross-sectional study design was used to evaluate softball batting measures among collegiate softball players. Eighteen collegiate softball players from across Ontario were recruited to participate. Participants were divided into two groups: those with previous concussion (n = 7; mean age, 20.7 years; mean time since last concussion, 3.9 years) and those without (n = 11; mean age, 20.4 years). Pitch recognition, swing timing, and swing decision making were based on participants responses to pre-recorded pitching videos. Pitch recognition, swing timing, and swing decision making were similar between groups. There was not a significant correlation between the computerized RT and swing RT. These results suggest that collegiate softball players with less than three concussions perform similarly to those without concussion for softball cognition and swing timing when tested an average of 3.9 years post-concussion.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".