9.15 Effect of postural control and exertion on dynamic visual acuity in athletes with and without history of sport-related concussion
Bibliographic record
Abstract
Objective To investigate the effects of posture and level of exertion on dynamic visual acuity (DVA; visual perceptual processing task) in athletes with and without history of sport-related concussion (SRC). Design DVA, scored as the log of the minimum angle of resolution (LogMAR), was assessed using a custom program with a tumbling ‘E’ target displayed on a 55’ monitor at a 4m viewing distance. The target moved randomly or horizontally at 2.31m/s. Participants completed one trial of each motion in four conditions: seated; standing; and treadmill walking at low (85–100 bpm) and moderate (115–130bpm) intensities. Setting University Research Laboratory. Participants Varsity athletes (N= 28; age= 20.9±1.4; rugby, ice hockey, and lacrosse) without SRC (n=11) were compared to asymptomatic athletes with recent (n=8; post-SRC=3–9 months) and previous SRC (n=9; post-SRC=1–5 years). Interventions (or Assessment of Risk Factors) moV& program (V&MP Vision Suite, University of Waterloo). Outcome Measures Difference in LogMAR scores from seated were calculated for the other postural conditions. Main Results There was no interaction effect of motion and posture (F=1.29, p=.278), no main effect of motion (F=0.33, p=.573) or posture (F=2.10, p=.133). There was a main effect of group (F= 5.09, p< .05; f= 0.25), indicating that athletes with recent and previous SRC obtained worse DVA scores compared to athletes without SRC across all postural conditions. Conclusions An objective DVA assessment in progressive postural and exertional conditions may be sensitive to differences in performance between athletes with and without history of SRC, even after resolution of symptoms and return to sport.
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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 | 0.001 |
| 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) | 0.004 | 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".