The Effectiveness of Interventions to Reduce Neurological Injuries in Rugby Union: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Rugby is characterized by high-speed collisions among the players that predispose them to injuries, particularly to the head, neck, and spine. OBJECTIVE: To evaluate the effectiveness of current neurological injury prevention strategies in rugby union. METHODS: Systematic review in May 2010. We assessed the quality and content of studies that evaluated injury prevention strategies for rugby players and reported on neurological outcomes. We searched OVID Medline, OVID HealthStar, CINAHL, Sport Discus, PubMed, Scholar's Portal Physical Education Index, Web of Science, and the Cochrane Controlled Clinical Trials Register (CENTRAL) and conducted a manual search of the cited literature lists of each included study. RESULTS: Ten articles are included in the review, with 2 of these assessing both headgear and mouthguards. Four studies reported insignificant reductions in neurological injury with the use of headgear. The results of 4 studies on the effectiveness of mouthguards in preventing neurological injury were inconclusive. Four studies reported significant reductions in neurological injury after the implementation of nationwide multifaceted injury prevention strategies with a focus on education CONCLUSION: There is limited evidence to support the effectiveness of mouthguards and headgear in reducing neurological injuries; however, system-wide, mandatory interventions are useful in reducing neurological injuries in rugby.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".