The Relationship Between Neck Strength and Sports-Related Concussion in Team Sports: A Systematic Review With Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: We aimed to quantify the relationship between neck strength and sports-related concussion (SRC) for athletes participating in team sports. DESIGN: Etiology systematic review with meta-analysis. LITERATURE SEARCH: PubMed, PsycINFO, MEDLINE, CINAHL, CENTRAL, and Scopus were searched on March 17, 2022, and updated on April 18, 2023. STUDY SELECTION CRITERIA: Team sports where an opponent invades the player’s territory (eg, football, rugby, basketball) that reported at least 1 measure of neck strength, and 1 measure of SRC incidence, using cohort, case-control, or cross-sectional study designs. The Newcastle-Ottawa scale was used to assess risk of bias; certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation approach. DATA SYNTHESIS: Studies were summarized qualitatively and quantitatively. To understand the relationship between neck strength and future SRC incidence, random-effects meta-analysis was conducted on prospective longitudinal studies. RESULTS: From a total of 1445 search results, eight studies including 7625 participants met the inclusion criteria. Five studies reported a relationship between greater neck strength or motor control and reduced concussion incidence. Pooled results from 4 studies indicated small ( r = 0.08-0.14) nonsignificant effects with substantial heterogeneity (I 2 >90%). The considerable heterogeneity is likely a result of synthesized studies with vastly different sample characteristics, including participant age, playing level, and sports. CONCLUSIONS: There was very low–certainty evidence suggesting a small, nonsignificant relationship between greater neck strength and a lower risk of sustaining a SRC. J Orthop Sports Phys Ther 2023;53(10):585-593. Epub: 10 July 2023. doi:10.2519/jospt.2023.11727
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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 | 0.024 | 0.063 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.043 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".