Association of artificial turf and concussion in competitive contact sports: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence of head injuries and concussion in contact sports, comparing natural grass with artificial turf surfaces. DESIGN: Systematic review and meta-analysis via the RevMan V.5.3 software. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: All studies describing competitive contact sports played on both natural grass and artificial turf. The primary outcome measured was occurrence of head injury and concussion. DATA SOURCES: The databases include PubMed, Embase, Cochrane, Medline and Sport Discus. The last search took place on 23 May 2019. The Newcastle-Ottawa Quality Assessment Scale evaluated the methodological quality of the selected studies with a funnel plot designed to determine publication bias. Study screening and data extraction were performed by two independent reviewers. RESULTS: Initial screening generated 42 publications, with 12 meeting criteria for inclusion. Eight studies described concussion only. The rate ratio (RR) of head injury and concussion was less on artificial turf compared with natural grass (RR=0.89, 95% CI 0.77 to 1.04) as was the rate ratio of concussion only (RR=0.72, 95% CI 0.58 to 0.89). CONCLUSION: Analysis of published data demonstrates a decreased incidence of head injury and concussion when contact sports are played on artificial turf. This difference was most marked for sports such as rugby and American football. However, artificial turf has no association with the incidence of head injury or concussion while playing soccer.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".