801 EP014 – Enhancing concussion prevention: findings from an international survey of French-speaking athletes on sport-related concussion characteristics
Bibliographic record
Abstract
Background Sport-related concussions (SRCs) pose significant physical, emotional, and economic burdens on athletes and the sports community. With the long-lasting consequences of SRCs, effective prevention strategies are imperative. Objective To investigate the relationship between demographic data, history of concussion and sports’ characteristics with the number of SRC, symptoms’ duration and resting period. Design Cross-sectional online survey Setting A three-month online survey was conducted among athletes in five French-speaking countries. The survey was created on an online platform and distributed through institutional networks, professional associations, regional clubs, and research centers. Communication efforts included newsletters and social media platforms. Participants Athletes over 14 years old. Main Outcome Measurements Data on demographic, history of concussion, sport category (high-risk, moderate-risk, low-risk), sport level, symptoms duration and recovery duration were extracted from the database. We investigated if the number of SRC, duration of symptoms and duration of recovery period differ depending to demographics, sport category and sport level. Results Out of the 998 participating athletes, 939 complete answers were analysed with balanced gender distribution. The duration of symptoms was longer for women (132.54±21.34 days) compared to men (81.37±19.93 days; z=-4.674, p<0.001). Athletes from Canada had higher number of repeated SRCs (23 athletes with 4 and 11 athletes with 5 SRCs) than European countries (7 athletes with 4, and one athlete with 5 SRCs) (X2=39.718, p<0.001), with symptoms lasting 88 days longer on average (p<0.001) in similar population. The number of SRCs was influenced by the sport category. A positive correlation was observed between the number of SRCs and symptom duration. Professional athletes had shorter recovery periods than non-professionals. Conclusions These findings highlight the need for tailored prevention strategies in high-risk sports and among athletes. Additionally, the prolonged symptom duration exceeding recommended recovery periods raises crucial concerns in SRC management, emphasizing the importance of preventive 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".