Neuropsychological Assessment of Professional Ice Hockey Players: A Cross-Cultural Examination of Baseline Data Across Language Groups
Bibliographic record
Abstract
OBJECTIVE: Neuropsychological testing in sports has become routine across all levels of play. The National Hockey League (NHL) has conducted baseline neuropsychological assessment of all players since 1997. This study seeks to examine baseline differences among linguistically and culturally diverse groups within the NHL and to present comprehensive normative data for these groups. METHOD: Baseline data were obtained from 3,145 professional hockey players' baseline symptom reporting, neuropsychological test performance on a battery of traditional "paper and pencil" measures, and self-reported concussion history. In addition, 604 baseline post-injury paper and pencil evaluations were conducted the season following a concussion and 4,780 computerized baseline ImPACT administrations were obtained following the introduction of computerized testing. RESULTS: Normative data for paper and pencil tests and ImPACT are presented for the major language groups within the league: English, French, Swedish, Russian, Czech, Finnish, and German (ImPACT only). It was found that symptom reporting, the number of concussions sustained, and neuropsychological test results vary significantly based on a players' language of origin. This variability was also present when players were tested in their language of origin. CONCLUSIONS: This study provides insight into the significant baseline differences that exist among NHL players regarding symptoms, concussion history, and cognitive functioning. The findings are discussed with respect to the evaluation and management of NHL players who sustain concussion and more generally in the context of neuropsychological assessment in cross-cultural settings, including the importance of examining neuropsychological functioning using culturally specific norms.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".