11.27 Dynamic neck strength as a potential screening tool for future concussion risk: a pilot study
Bibliographic record
Abstract
Objective Women’s soccer is a high-risk sport for concussion. The strongest predictor for future concussion risk is a prior history of concussion. Neck strength may be protective against concussion. Do female soccer players with a prior history of concussion demonstrate decreased neck strength? Design Observational cohort. Setting University soccer team. Participants 28 players. Assessment of Risk Factors Independent = history of concussion, dependent = dynamic neck strength. Outcome Measures Dynamic neck strength measured as peak Rate of Force Development (RFD) using the TopSpin360. Self-declared concussion history was recorded. Independent samples t-tests were used to compare differences between groups. Main Results 10 players declared a history of concussion (HxCon), 18 self-declared without (NoHxCon). There was a significant difference in dynamic neck strength between the groups (HxCon = 3.8 lbf*s-1, 95%CI 2.5–5.2, NoHxCon = 7.1 lbf*s-1, 95%CI 5.2–9.1, p<.05). Area under the curve demonstrated a sensitivity of 72% and specificity of 80% at an RFD of 4.5 lbf*s-1 to distinguish players with a history of concussion from those without. Conclusions In this small study, dynamic neck strength was able to differentiate players with a history of concussion, and hence at a known higher risk of future concussion, from those with no history of concussion and relatively lower risk. Future studies should explore using dynamic neck strength as a potential screening tool to assess athletes’ risk of concussion.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".