9.9 Preseason measures of cervical function, vestibulo-ocular reflex, dynamic balance, and divided attention in youth ice hockey players: typical scores and the effect of concussion history
Bibliographic record
Abstract
Objective Evaluate differences in preseason cervical spine, vestibulo-ocular reflex, dynamic balance, and divided attention measures in competitive youth ice hockey players who report a previous concussion compared to those who do not. Design This study was a secondary analysis of data from two prospective cohort studies conducted in Calgary and Edmonton, Alberta, Canada from 2011 to 2018. Setting Youth ice hockey arenas in Alberta, Canada. Participants Uninjured male and female youth ice hockey players, aged 10 – 18 years. Assessment of Risk Factors Self-reported previous history of concussion. Outcome Measures Scores on measures of the cervical spine [Cervical Flexor Endurance (sec), Cervical Flexion-Rotation Test (normal/abnormal), Anterolateral Cervical Spine Strength (lbs), Head Perturbation Test (/8) and Joint Position Error (cm)], vestibulo-ocular reflex [Dynamic Visual Acuity (LogMAR), Head Thrust Test (Positive/Negative)], dynamic balance [Functional Gait Assessment (/30)] and divided attention [Walking-while-talking test (sec)]. Main Results A total of 2311 participants were included in this study [87.19% male (n=2015), 12.81% female (n=296), 38.96% reported a previous concussion (n=785)]. Using multivariable linear or logistic regression analyses (appropriate for each outcome), adjusting for age-group, sex, level of play, and clustered by team, there were no differences by concussion history in any of the measures. Descriptive statistics appeared to demonstrate differences by sex and age in some measures. Conclusions Concussion history was unrelated to measures of cervical function in youth ice hockey players. Further prospective research involving incident cases across other sports is warranted to better understand how a history of concussion may affect scores on these outcome measures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".