3.31 Can clinical measures identify sport-related concussions in youth ice hockey players?
Bibliographic record
Abstract
Objective To determine if Sport Concussion Assessment Tool (SCAT) subcomponents and clinical measures (cervical-spine, ocular-motor, vestibulo-ocular, dynamic-balance, and divided-attention) can discriminate between preseason and post-concussion states in youth ice hockey players. Design Diagnostic accuracy study. Setting Ice hockey rinks and sport medicine centre (Alberta, Canada) Participants Youth ice hockey players [n=338; 48 females (14.20%), ages 10–17] who participated in two cohort studies (‘Elite ice hockey’ 2011–2012; ‘Safe2Play’ 2013–2018). Assessment of Risk Factors Players completed a SCAT3/5 and clinical measures at preseason and post-SRC at time of diagnosis. Outcome Measures SRC was defined per the 4th/5th Consensus on Concussion in Sport. SCAT3/5 [symptom severity score (SSS,/132), modified balance error scoring system (mBESS,/30), etc.], cervical range of motion (ROM; full/limited), cervical flexor endurance (CFE; seconds), cervical flexion rotation test (CFRT; positive/negative), anterolateral cervical spine strength (CSpStrength; lbs), head perturbation test (HPT;/8), extra-ocular motion (EOM; normal/abnormal), head thrust test (HTT; positive/negative), clinical dynamic visual acuity (DVA; logMAR), Functional Gait Assessment (FGA;/30), Walking while talking test (WWTT; seconds) were assessed by a physiotherapist/athletic therapist. Diagnostic accuracy statistics were calculated and used to inform selection of variables for a multivariable model, with a cut-point for correctly classifying concussion of 55%. Main Results A model including SSS, HPT, WWTT complex, FGA, and CSpStrength possessed a sensitivity=0.73; specificity=0.86; Likelihood ratio (LR)+=5.37, LR-=0.31, and Area Under the Curve=0.85 for discriminating between preseason state and SRC-diagnosis. Conclusions The diagnostic accuracy of a combination of SSS and clinical measures (cervical-spine, divided-attention and dynamic-balance FGA) was high based on sensitivity and specificity in discriminating between preseason state and SRC-diagnosis in youth hockey players.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".