Youth Preseason Performance on the Sport Concussion Assessment Tool 5 Across Multiple Sports
Bibliographic record
Abstract
OBJECTIVE: To examine preseason Sport Concussion Assessment Tool 5 (SCAT5) performance of adolescent sport participants by environment (in-person/virtual), sex, age, concussion history, collision/noncollision sport participation, and self-reported medical diagnoses. DESIGN: Cross-sectional. SETTING: Canadian community and high-school sport settings. PARTICIPANTS: Three thousand eight hundred five adolescent (2493 male, 1275 female, and 37 did not disclose; 11- to 19-year-old) sport participants. ASSESSMENT OF RISK FACTORS: Sport Concussion Assessment Tool 5 administration method (in-person/virtual), sex (male/female/unreported), age (years), concussion history (0/1/2/3+), collision/noncollision sport participant, and self-reported medical diagnoses [attention deficit disorder or attention-deficit/hyperactivity disorder, headache/migraine, learning disability, and psychiatric disorder (ie, anxiety/depression/other)]. OUTCOME MEASURES: Preseason SCAT5 outcomes including total number of symptoms (TNS; /22), symptom severity score (SSS; /132), Standardized Assessment of Concussion (SAC; /50), and modified Balance Error Scoring System (mBESS; /30). RESULTS: Multiple multilevel linear or Poisson regression complete case analyses adjusting for clustering and robust standard errors, with β-coefficients (95% CI) back-transformed to indicate an increase/decrease in SCAT5 subdomains when relevant for clinical interpretation. Virtual (V) performance was associated with fewer symptoms reported [TNS Difference V-IP = -1.53 (95% CI, -2.22 to -0.85)], lower SSS [-2.49 (95% CI, -4.41 to -0.58)], and fewer mBESS errors (IP) [-0.52 (95% CI, -0.77 to -0.27)] compared with in-person. For every one-year increase in age, more symptoms [TNS = 0.22 (95% CI, 0.01-0.44)], higher SSS [0.52 (95% CI, 0.01-1.06)], higher SAC [0.27 (95% CI, 0.15-0.38), and poorer balance [mBESS = -0.19 (-0.28 to -0.09)] were observed. Differences between males and females were also seen across all SCAT5 outcomes. Individuals reporting any medical diagnosis or 3+ concussion history also reported more symptoms (TNS) and higher SSS than those who did not. CONCLUSIONS: Administration environment, sex, age, concussion history, and medical diagnoses were associated with SCAT5 subdomains and are important considerations when interpreting the SCAT5 results.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".