Soccer-related head injuries—analysis of sentinel surveillance data collected by the electronic Canadian Hospitals Injury Reporting and Prevention Program
Bibliographic record
Abstract
BACKGROUND: Participating in sports is a great way to gain physical, psychological, and social benefits. However, it also carries the risk of injury. Soccer is one of the most popular sports worldwide, and in recent years, there have been concerns about potential vulnerabilities to head injuries. OBJECTIVES: To investigate soccer-related head injuries (SRHIs), using data from the electronic Canadian Hospitals Injury Reporting and Prevention Program (eCHIRPP) surveillance system. Specifically, we aim to compare characteristics of SRHI cases to all head injury cases within the eCHIRPP database. METHODS: Descriptive analyses of emergency department (ED) injury surveillance data (2011 to 2017) for individuals aged 5 to 29 years from all participating eCHIRPP sites. Computation of proportionate injury ratios (PIR) comparing SRHIs to all head injuries reported to eCHIRPP, and 95% confidence intervals (CI). RESULTS: A total of 3,970 SRHIs were reported to eCHIRPP. Injuries were from contact with another player, the ball, ground, goal-post, and other causes. Of the injuries caused by contact with the ball, 9% were from purposely directing the ball with the head (heading). A higher proportion of concussions (PIR=1.32, 95% confidence interval [CI]: 1.27 to 1.37) and minor closed head injuries (PIR=1.20, 95% CI: 1.15 to 1.26) were observed in soccer players. Higher proportions of head injuries occurred in organized soccer and soccer played outdoors. However, admission to the ED for a SRHI was rare (PIR=0.40, 95% CI: 0.30 to 0.55). CONCLUSIONS: Overall, elevated proportions of brain injuries were observed among soccer players, however, these injuries were unlikely to result in a hospital admission. Moreover, purposely heading the ball contributed to few ED visits.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".