Use of the Sport Concussion Assessment Tool 5 (SCAT5) in professional hockey, part 1: cross-cultural normative data
Bibliographic record
Abstract
OBJECTIVES: This paper provides comprehensive normative data stratified by language preference and age on the components of the National Hockey League (NHL) Sport Concussion Assessment Tool 5 (SCAT5) in a multilingual sample of professional ice hockey players and compares the findings from a paper form of the NHL SCAT5 with an electronic (App) version of the tool. METHODS: A total of 1924 male NHL and American Hockey League (AHL) players (ages 17-41) were assessed during preseason medical evaluations (baseline); 1881 were assessed with the NHL SCAT5 App via tablet and 43 received the paper version of the NHL Modified SCAT5. RESULTS: No significant differences between the App and paper modes of administration emerged in a subsample of English preference players. Significant SCAT5 differences among language preference groups emerged on measures of cognitive functioning (Immediate Memory,Concentration). No language preference differences emerged on the Delayed Recall component. Using age as a continuous variable, older participants outperformed younger players on Immediate Memory, Delayed Recall and Concentration. Players wearing skates demonstrated significantly more modified Balance Error Scoring System (mBESS) total errors than barefoot players. Normative data tables for language preference groups are presented. CONCLUSIONS: Significant differences were found between English and non-English language preference groups on the components of SCAT5, which suggest that language-specific normative data, rather than aggregated normative data, are preferable when interpreting test scores. Similarly, age-specific normative data tables may provide greater precision in data interpretation. Due to clear ceiling effects on the mBESS single leg and tandem stances, players should not be tested while wearing skates.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".