328 The role of neck strength in mitigating sport related concussion: a systematic review and meta-analysis
Bibliographic record
Abstract
Background In recent years there has been an increase in focus on the potential role neck muscle strength and strengthening may play in helping to mitigate the risk of sports related concussion (SRC). However, to date there has not been any systematic reviews or analysis to help quantify this role and provide guidance. Objective To systematically review the literature surrounding the neck strength and strengthening in recusing the risk of SRC. Design Systematic review and meta-analysis. Data sources SportsDISCUS, Ovid Medline, Web of Science, CINAHL and EMBASE Patients (or Participants) Athletic population regardless of age or sex. Study selection The above databases were searched using a combination of keywords and medical subject headings to identify studies that examined the association between SRC and neck strength and or neck strengthening programs. Results The initial search produced 593 studies, of which 6 were included for review and meta-analysis. Intervention programs that included neck strengthening were shown to be effective at decreasing the incidence of SRC RR 0.54 (95% CI 0.50–0.95) Conclusions Neck strengthening intervention programs can reduce the incidence of SRC in an athletic population. Athletes who participate in high-risk sports or are from high-risk populations (i.e. adolescents and females) should incorporate neck strengthening into their respective training programs.
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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 | 0.022 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".