Examining Sport Concussion Assessment Tool ratings for male and female youth hockey players with and without a history of concussion
Bibliographic record
Abstract
BACKGROUND: Concussion is one of the most commonly occurring injuries in sport today. The Sport Concussion Assessment Tool (SCAT) is a commonly used paper neurocognitive tool. To date, little is known about SCAT baseline normative values in youth athletes. OBJECTIVE: The purpose of this study was to determine normative values on the SCAT for male and female youth hockey players. METHODS: This is a secondary data analysis of pooled data from three prospective cohort studies examining the risk of injury in paediatric ice hockey players aged 9-17 years. A preseason baseline demographic and injury history questionnaire was completed by each player. RESULTS: A total of 4193 players completed SCATs at baseline and were included in the analysis. 781 players (18.6%) reported a previous history of concussion. Fatigue and low energy followed by headache were the most commonly reported symptoms in all players. The majority of youth players could recite all five words immediately but only three words when delayed. A smaller proportion of the males were able to report the months of the year in reverse order compared with females of a similar age. The median number of digits recited in reverse order was 4. CONCLUSIONS: Youth ratings varied between age groups, gender and from previously reported ratings of varsity athletes, possibly reflecting developmental and gender differences. An understanding of these differences in youth athletes is important to ensure appropriate performance expectations on the SCAT and when making clinical decisions following a concussion.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".