Use of the Sport Concussion Assessment Tool 5 (SCAT5) in professional hockey, part 2: which components differentiate concussed and non-concussed players?
Bibliographic record
Abstract
OBJECTIVES: To examine the utility of Sport Concussion Assessment Tool (SCAT5) subcomponents in differentiating physician diagnosed concussed players from controls. METHODS: We evaluated 1924 professional hockey players at training camp using the National Hockey League (NHL) Modified SCAT5 prior to the 2018-2019 season. Over the course of the season, 314 English-speaking players received SCAT5 evaluations within 1 day of a suspected concussive event. Of these players, 140 (45%) were subsequently diagnosed with concussion by their team physicians. RESULTS: Concussed players reported more symptoms (Concussed M=8.52, SD=4.78; Control M=3.32, SD=3.97), and recalled fewer words than Controls on both the Immediate Memory (Concussed M=19.34, SD=4.06; Control M=21.53, SD=2.94) and Delayed Recall (Concussed z=-0.91; Control z=-0.09) tasks during the acute evaluation. Concussed players also made more errors than Controls on the mBESS and were more likely to report double vision and exhibit clinician-observed balance problems than controls. There were no between-group differences on the Concentration component of the SCAT5. Stepwise regression revealed that symptom report and list learning tasks both accounted for independent variance in identifying players diagnosed with concussion. CONCLUSIONS: These findings provide support for use of the SCAT5 to assist in identifying concussed professional hockey players. When examining SCAT5 subtests, both symptom report and the 10-item word list accounted for independent variance in identifying concussion status in this sample of professional hockey players. The mBESS also differentiated Concussed players and Controls. The Concentration component of the SCAT5 did not significantly differentiate Concussed players and Controls.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".