Determining Sport Concussion Assessment Tool fifth Edition (SCAT5) reliable change in male professional hockey players
Bibliographic record
Abstract
OBJECTIVES: The purpose of this paper is to provide reliable change metrics and assess the test-retest psychometrics of the Sport Concussion Assessment Tool fifth Edition (SCAT5) in a multilingual sample of professional ice hockey players. METHODS: The SCAT5 was administered to National Hockey League and American Hockey League male professional ice hockey players as part of the 2018 and 2019 season medical evaluations. We extracted data from an existing clinical database of players who were administered two consecutive baseline evaluations and had no intervening concussions to assess psychometric properties (eg, test-retest reliability) and develop reliable change metrics. RESULTS: Overall, 1007 players met inclusion criteria. Players were reassessed on average 344.52 (±62.52) days following their first baseline. Test-retest reliability ranged from r=0.3 to r=0.5. Reliable change metrics for Immediate Memory varied considerably dependent on form order. Significant differences were found between English versus non-English language preference on Immediate Memory and Concentration but not Delayed Memory or the modified Balance Error Scoring System (mBESS). Reliable change indices at the 90% CI were: Symptom Severity (5), Total Symptoms (3), Immediate Memory (3-8), Concentration (2), mBESS (5) and Delayed Memory (2-4). CONCLUSIONS: Our findings highlight the importance of considering form differences and language preference when determining reliable change from baseline on the SCAT5 in male professional hockey players. The relatively modest test-retest reliability of the measures leaves room for improved psychometric properties and increased sensitivity to concussion in future iterations of the SCAT.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".