Epidemiology of concussion in sport: a literature review
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to summarize sport concussion incidence data, identify sports that present higher injury frequency, reveal the degree of risk in some lesser-known sports, and outline specific details within the sports literature that raise additional concerns, such as helmet-to-helmet contact and player positions that experience frequent impact. METHODS: A systematic literature review of Pub Med using keyword search on injury, concussion, and sports was performed through May 2012. Abstracts were identified, selections were made based upon inclusion criteria, and full-length articles were obtained. Additional articles were considered following review of reference sections. Articles were reviewed and tabulated according to sport. RESULTS: Two hundred eighty-nine articles were screened, and 62 articles were reviewed. The overall incidence of concussion in sport ranged from 0.1 to 21.5 per 1000 athletic exposures. The lowest incidence was reported in swimming and diving. Concussion incidence was highest in Canadian junior ice hockey, but elevated incidence in American football remains a concern because of the large number of participants. CONCLUSIONS: The literature reviewed included incidence of concussion on the field of play under real-world conditions and influenced by the current culture of sport. The studies examined in this article show that there is risk of concussion in nearly every sport. Some sports have higher concussion frequency than others, which may depend upon the forces and roles of the positions played in these sports. Younger athletes have a higher incidence of concussion, and female incidence is greater than male in many comparable sports. Headgear may reduce concussion in some sports but may also give athletes a false sense of protection.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".