Does the Concussion Clinican Examination Predict Post-Concussion Subsequent Musculoskeletal Injury?
Bibliographic record
Abstract
Objective To identify post-concussion subsequent lower extermity musculoskeletal injury predictors from a clinical concussion assessment. Background Emerging evidence has identified an elevated risk of subsequent lower extremity musculoskeletal injury (LE-MSK) in the year post-concussion. This approximately 2-fold elevated risk has been identified in diverse populations including high school, college, professional sports and military populations. While persistent impairments in postural control are a speculated mechanism, these assessments require instrumented biomechanical measures; thus, there are currently no clinically feasible predictors which have been identified. Design/Methods Eighty three NCAA Division I student-athletes (51.8% female, ht: 1.75 +/− 0.12 m, wt: 76.2 +/− 20.1 kg) who had suffered a sports related concussion (LOC: 7.5%, PTA: 12.2%) with baseline data were assessed within 72 hours post-concussion were included in this retrospectic study. The clinical examination consisted of the Balance Error Scoring System (BESS), Standard Assessment of Concussion (SAC), Clinical Reaction Time (CRT), King-Devick (KD) and the Immediate Post-Concussion Assessment and Cognitve Test (ImPACT) composite values. Change scores (Δ) from baseline to acute post-concussion served as predictors to identify subsequent LE-MSK with a step wise binary logistic regression. Results The subsequent LE-MSK rate was 72.3% (60/83). The overall model failed to identify a predictive relationship between change scores and subsequent injury risk (r2 = 0.242, p = 0.458). Exploratory testing failed to identify any significant individual predictors of subsequent LE-MSK: BESS: Δ = −0.35 errs, p = 0.964; SAC: Δ = −0.55, p = 0.239; ImPACT Verbal Memory: Δ = −0.53, p = 0.324; ImPACT Visual Memory: Δ = −5.6, p = 0.750; ImPACT Motor Speed: Δ = −2.81, p = 0.070; ImPACT Reaction Time: Δ = 0.042 sec, p = 0.164; CRT: Δ = 12.2 ms, p = 0.564; and KD: Δ = 2.8 sec, p = 0.607. Conclusions None of the common clinical concussion assessments were a significant predictor of subsequent LE-MSK potentially due to a high injury rate (72.3%). Future research should continue attempts to idenify clinically feasible predictors to allow clinicians to identify at-risk athletes to engage injury prevention strategies to reduce subsequent LE-MSK.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".