Ten-Word List Performance in Healthy Athletes and Athletes at 3-to-5 Days Following Concussion
Bibliographic record
Abstract
OBJECTIVE: Although the word recall component of the Sport Concussion Assessment Tool (SCAT) was updated from 5-to-10 words, its clinical utility across athletic populations remains unknown. The purpose of this study was to provide normative data on the SCAT-5 10-word test and examine its discriminative ability between healthy university level athletes and those with sport-related concussion (SRC). DESIGN: Cross-sectional. SETTING: University. PARTICIPANTS: Three hundred sixty-three (363, M = 220, F = 153) healthy athletes and 49 (M = 30, F = 19) athletes following SRC. INDEPENDENT VARIABLE: Healthy athletes measured preseason were compared with athletes following an SRC (median = 4.0 days postinjury). MAIN OUTCOME MEASURE: Ten-word list performance from the SCAT-5. RESULTS: There were no significant differences in either immediate ( P = 0.228, Cohen's D = 0.18) or delayed ( P = 0.908, Cohen's D = 0.02) recall tests between athletes with SRC and healthy athletes; the null findings extended to both the male and female subset comparisons. Among healthy athletes, females outperformed males on both immediate (mean difference = 1.0, P < 0.001) and delayed (mean difference = 0.5, P = 0.006) recall tasks. CONCLUSION: Concussion does not seem to impact immediate or delayed recall of the 10-word list in the subacute period following injury. Practitioners should be mindful of sex differences and the time point of administration following injury.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".