Are pre-season reports of neck pain, dizziness and/or headaches risk factors for concussion in male youth ice hockey players?
Bibliographic record
Abstract
Background Concussion is a commonly encountered injury associated with potential long-term sequelae. No previous studies have evaluated dizziness, neck pain and headache as potential risk factors for concussion. Objective The objective of this study is to determine the risk of concussion in male youth hockey players with preseason reports of neck pain, headaches and dizziness. Design This study is a secondary data analysis of a prospective cohort study examining the risk of injury associated with body checking among paediatric ice hockey players. Setting Youth ice hockey in Alberta and Quebec, Canada. Participants A total of 3902 11–14 year old males from 282 teams participated. Assessment of risk factors Each participant completed a pre-season baseline demographic and injury history questionnaire. Preseason reports of neck pain, headache or dizziness were documented on the Sport Concussion Assessment Tool. Main outcome measurements Diagnosed concussions were recorded during the season of play via a previously validated, prospective injury surveillance system. Results A total of 178 concussions occurred during the studies, with 11 players sustaining two concussions. Incidence rate ratios were calculated using Poisson regression, adjusted for exposure hours, cluster by team and potential covariates. Dizziness was not a significant predictor of concussion. Individuals reporting a headache or neck pain at the start of the season were 1.48 (95% CI 1.02 to 2.14) and 1.69 (95% CI 1.16 to 2.44) times more likely to suffer a concussion during the season than those not reporting these symptoms. Individuals reporting any two of dizziness, headache and neck pain were 1.99 (95% CI 1.20 to 3.32) times more likely to sustain a concussion. Conclusion Male youth athletes reporting headache or neck pain at baseline were at an increased risk of concussion during the season. From an injury prevention perspective, baseline testing may aid in identifying individuals at a higher risk for 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".