327 Lower dynamic neck strength is associated with history of concussion in varsity female soccer players
Bibliographic record
Abstract
Background There is mounting evidence that dynamic neck strength may play a role in protecting against concussion. It is also well established that athletes with a prior history of concussion are at higher risk than those with no prior history. Objective To assess if there is a difference in dynamic neck strength between athletes with a self-declared history of concussion (HxC) and athletes with no history of concussion (No-HxC). Secondly, to determine if dynamic neck strength can be used as a predictor for previous concussion history through a receiver operating characteristic curve (ROC) and hence, be used as a proxy for future concussion risk. Design Observational cohort design Setting Varsity level female competitive soccer players Participants 28 athletes (average age 19.4 years, range 18–21), separated by self-declared history of concussion (HxC n=10 and No-HxC n=18) Assessment Dynamic neck strength was calculated as the peak Rate of Force Development (RFD) in pounds-force per second (lbf *s-1) achieved during 50 revolutions on the TopSpin360 neuromuscular neck-training device. Results RFD for HxC was 3.85 lbf *s-1 (95% CI 2.53 - 5.17 lbf *s-1) while RFD for No-HxC was 7.14 lbf *s-1 (95% CI 5.17 – 9.12 lbf *s-1) Independent samples t test p = 0.012. ROC cut-off value of 4.5 lbf *s-1 provides a sensitivity of 72% and specificity of 80% for detecting those with a history of concussion. Conclusions In this pilot study of varsity female soccer athletes, those with a history of concussion demonstrate significantly lower dynamic neck strength measurements compared to teammates with no history of concussion. Knowing that HxC athletes are at higher risk of future concussion, the ROC cut-off value of 4.5 lbf *s-1 provides a starting point for future studies using dynamic neck strength values for assessing baseline concussion risk in athletes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".