Concussions and heading in soccer: A review of the evidence of incidence, mechanisms, biomarkers and neurocognitive outcomes
Bibliographic record
Abstract
BACKGROUND: Soccer is currently the most popular and fastest-growing sport worldwide. Similar to many sports, soccer carries an inherent risk of injury, including concussion. Soccer is also unique in the use of 'heading'. The present paper provides a comprehensive review of the research examining the incidence, mechanisms, biomarkers of injury and neurocognitive outcomes of concussions and heading in soccer. METHODS: Seven databases were searched for articles from 1806 to 24 May 2013. Articles obtained by the electronic search were reviewed for relevance, with 229 selected for review. Ultimately, 49 articles met criteria for inclusion in the present review. RESULTS: Female soccer players have a higher incidence of concussions than males. The most frequent injury mechanism is player-to-player contact for both genders. Few studies examined the effects of concussion in soccer players; however, neurocognitive outcomes were similar to those reported in the larger sport concussion literature, while the effect of heading is less clear. CONCLUSION: Despite variation in research designs and study characteristics, the outcomes of concussions in soccer align with the greater concussion literature. This review makes recommendations for future research to increase standardization of research for improved understanding of concussions in soccer as well as the effects of heading.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".