Graded treadmill running protocol on dynamic visual acuity and balance for athletes with and without recent concussion history
Bibliographic record
Abstract
Abstract Athletes interpret dynamic visual scenes quickly and accurately during physical exertion. It is important to understand how increased exertion may impact vision and cognition following sport-related concussion (SRC). Purpose: To examine the effect of treadmill exercise on dynamic visual acuity (DVA) for athletes with and without SRC. Methods:Varsity athletes following recent SRC (CONC=12) were compared to athletes without SRC (ATHLETE=19). The DVA task presented a Tumbling ‘E’ target in four possible orientations during random walk (RW) or horizontal (H) motion at a speed of 30°/s. Participants performed DVA trials standing on a force plate (1000Hz) at four time points: 1) pre-exercise (PRE-EX), 2) immediately (POST1), 3) 10-minutes (POST10), and 4) 20-minutes post- exercise (POST20). Performance was calculated as a change in DVA score from PRE-EX and median response time (RT, ms). Balance control was analyzed using the root mean square of centre of pressure displacement (dCOP). Results: Both groups maintained DVA scores for both motion types with similar balance strategies post-exercise. CONC responded more slowly by POST10 and POST20 during RW-motion compared to ATHLETE. Conclusion: Both groups elicited similar exercise-induced benefits on DVA following exercise. However, a portion of athletes with SRC may experience transient cognitive benefits following exercise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".