An evaluation of the impact of <scp>FIFA</scp> World Cup on soccer emergency department injuries among Montreal adolescents
Bibliographic record
Abstract
AIM: The 'trickle-down effect', or how major sports events have a positive impact on sports participation, has been the subject of many studies, but none produced conclusive results. We took a different approach and rather than look at sports participation, we used injuries as a proxy and see if injuries increased, or remained the same, after the International Federation of Association Football World Cup. METHODS: Using a retrospective cohort design, we looked at the injuries suffered by males and females (13-16 years old) while playing team sports in Montreal, that occurred in May to July, from 1999 to 2014. Information reported by the Canadian Hospitals Injury Reporting Prevention Program (CHIRPP) was limited to the two CHIRPP centres in Montreal: the Montreal Children's Hospital and Hopital Sainte-Justine. RESULTS: In females, no significant trends were noticed. In males who played non-organised soccer, the percent changes between FIFA World Cup (WC) (June) and pre-FIFA WC (May) was always highest during FIFA WC years: 17.2% more injuries in years when FIFA WC was held compared to 1.3% less injuries during non-FIFA WC years. In non-organised soccer, male players suffered less strains/sprains (11.9% vs. 30.1%; P = 0.015), suffered more severe injuries (59.7% vs. 43.1%; P = 0.049) and more of their injuries were the results of direct contact with another player (26.8% vs. 13.3%; P = 0.028) during FIFA WC. CONCLUSION: FIFA WC seems to have an impact on the injuries of teenage boys when playing non-organised soccer. The impact was short-lived, only lasting during the FIFA WC event.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".