2.7 Does the SCAT5 10-word list improve the distribution of scores over the SCAT3 5-word list in professional hockey players?
Bibliographic record
Abstract
Objective The SCAT5 introduced Immediate Memory (IM) and Delayed Recall (DR)10-word lists to improve the ceiling effects found on SCAT3 5-word lists. We examined whether the 10-word lists improved the normative distribution of scores over the 5-word lists at baseline among professional hockey players. Design Retrospective Cohort. Setting Outpatient professional hockey. Participants English-language preference professional hockey players completed the SCAT3 5-word lists (n=1123) during pre-season in 2017 and SCAT5 10-word lists (n=1479) in 2019 for a total sample of 2602. Independent Variables 5-word vs. 10-word SCAT lists. Outcome Measures SCAT3 5-word IM and DR, SCAT5 10-word IM and DR scores. Main Results SCAT3 5-word IM lists produced a significant total score ceiling effect (M=14.62, md=15). A perfect score of 15 was achieved by 809 players (72%) on the 5-word list. In contrast, the SCAT5 10-word IM lists total scores were normally distributed (M=21.30, md=21). Only 1 player (0.1%) obtained a perfect score. Older players outperformed younger players (rs=.19, p<.001). Differences were found across language groups, F(7,1863)=7.14, p<.001, and form versions of the lists, F(2,1868)=62.41, p<.001. The 5-word DR component yielded similar distributions (M= 3.89, md=4) with 39% of players obtaining perfect scores. The 10-word mean DR score=7.02 (md=7). Again, older players outperformed younger players (rs=.12, p< .001). No language differences emerged. Conclusions The 10-word lists improved the distribution of scores on the SCAT5 IM/DR tasks, which should improve the detection of impaired performance following concussion. Significant differences were found in age, language preference and form.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".