Preseason Reports of Neck Pain, Dizziness, and Headache as Risk Factors for Concussion in Male Youth Ice Hockey Players
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine the risk of concussion in youth male hockey players with preseason reports of neck pain, headaches, and/or dizziness. DESIGN: Secondary data analysis of pooled data from 2 prospective cohort studies. SETTING: Ice hockey rinks in Alberta and Quebec, Canada. PARTICIPANTS: A total of 3832 male ice hockey players aged 11 to 14 years (280 teams) participated. ASSESSMENT OF RISK FACTORS: Participants recorded baseline preseason symptoms of dizziness, neck pain, and headaches on the Sport Concussion Assessment Tool. Incidence rate ratios (IRR) were estimated using Poisson regression, adjusted for cluster by team, hours of exposure, and other covariates. MAIN OUTCOME MEASURES: Concussions that occurred during the season were recorded using a validated prospective injury surveillance system. RESULTS: Preseason reports of neck pain and headache were risk factors for concussion (IRR = 1.67; 95% confidence interval [CI], 1.15-2.41 and IRR = 1.47; 95% CI, 1.01-2.13). Dizziness was a risk factor for concussion in the Pee Wee nonbody checking cohort (IRR = 3.11; 95% CI, 1.33-7.26). A combination of any 2 symptoms was a risk factor in the Pee Wee nonbody checking cohort (IRR = 3.65; 95% CI, 1.20-11.05) and the Bantam cohort (IRR = 2.40; 95% CI, 1.15-4.97). CONCLUSIONS: Male youth athletes reporting headache and neck pain at baseline were at an increased risk of concussion during the season. The risk associated with dizziness and any 2 of dizziness, neck pain, or headaches depended on age group and body checking. CLINICAL RELEVANCE: Baseline testing may be of benefit to identify individuals with symptoms of dizziness, neck pain, and headaches who may be at a higher risk of concussion during the season.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".