Validation of a Concussion Assessment Tool
Bibliographic record
Abstract
Objective Sports related concussions, or mild traumatic brain injuries, have been steadily increasing over the past two decades. Effective screening and identification of concussions play a critical role in the diagnosis and rehabilitation process. Although side line assessment tools are available, there are few well validated tests available to assist medical providers in this decision-making process. This study aims to determine whether previously validated tools which assess neurocognitive and neurophysiological abilities can predict concussion symptom endorsement in a sample of child and adolescent athletes. Background Participants were recruited from two settings: The office of a pediatric neurologist (seen 3 to 109 days post incident) and from preseason baseline assessments. Design/Methods Method: Participants were 113 individuals, aged 6 to 17, representing 84 consecutive cases of individuals completing standardized baseline assessments with no recent history of concussion, and 29 consecutive cases undergoing a post-concussion evaluation by a pediatric neurologist. Participants completed a standardized battery of tests comprised of the Connors’ Continuous Performance Test (CPT-3), the Balance Error Scoring System (BESS) and the NIH 4-meter Gait Test and completed a checklist of CDC concussion symptoms. Results The screening battery explained 33% of the variance (d = 1.4) in concussion symptom endorsement, after controlling for age. The neurocognitive test alone (CPT-3) accounts for 21.5% of the variance (d = 1.05) in symptoms after controlling for age, and the neurobehavioral measures (BESS and NIH 4m Gait) then account for an additional 11.5% variance (they account for 18.6% variance, d = 0.96, when entered first). These effect sizes are considered large to very large and reflect a marked increase in predictive validity relative to existing measures used in concussion assessments. Conclusions An easy to administer and relatively brief screening test can be used in medical settings to identify concussions and predict significant and substantial variability in CDC concussion symptoms.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".