A Narrative Review of Soccer-Related Concussion Management in Children and Adults Over the Past 10 Years
Bibliographic record
Abstract
Soccer-related concussions (SRC) have increased despite an overall reduction of concussions across all sports activities. Few papers have studied the mechanism of injury, and have been mostly done in high-income countries or focused on small populations, preventing generalization. Our goal was to analyze the available data published about SRC over the past 10 years, independent of the country's income level. A narrative review was performed. The definition of sport-related concussion from the American Academy of Neurology and studies published between 2013 and 2023 were used. Of 1210 articles, 45 met the inclusion criteria. The results showed that SRC was more frequent in females (57.6%) than males (44.3%). Player-to-player interaction was the most common mechanism of injury, with midfielders being the most affected position. The first providers to diagnose were certified athletic trainers, within the first 24 hours. Neurological evaluations, including SCAT (Sport Assessment Concussion Tool) and ImPact (Immediate Post-concussion Assessment and Cognitive Testing), were included in 42.2% of the studies, with SCAT and ImPact specifically used in 15.5% and 11% of cases, respectively. Need for hospitalization was found in 8.9% of participants and one player required surgical intervention. At the time of the concussion, confusion, dizziness, and amnesia were reported frequently. However, after the concussion, headaches and dizziness were prevalent. Follow-up data were included in 35.5% of the studies. On average, children missed 15 practice days and returned to school after 8 days. In conclusion, future research should focus on the circumstances around head-to-head injuries by age, sex, and level of professionalism as well as the importance of early diagnosis and careful follow-up, to protect the players and improve their outcomes.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".