Relationship between king devick TEST, SCAR3 and 3D mot in cognitive assessment
Bibliographic record
Abstract
Objective To examine the relationship between aspects of the Sport Concussion Assessment Tool 3 (SCAT3), the King-Devick Test (KDT) and Three-Dimensional Multiple Object Tracking (3D MOT) at baseline. Design Prospective design. Setting University resarch laboratory. Participants A convenience sample of 304 healthy, non-concussed, athletic participants (101 females, 203 males) ranging in age from 11.69 20.41 years (mean age=16.05 + 4.36) were included in the analysis. Outcome measures Participants completed the SCAT3, KDT and 3D MOT in a single visit. A regression analysis was performed to see if any aspects of the SCAT3 (immediate memory (IM), coordination (COOR), and delayed recall (DR)), and/or the KDT, predicted 3D MOT scores. Main results A multiple linear regression was calculated to see if KDT, IM, DR and COOR predicted the speed of the 3D MOT. The assumptions of multivariate regression were tested and corrections were applied as needed. Using the stepwise method, it was found that KD, DR and COOR explain a significant amount of the variance in the speed of the 3D MOT (F(3, 256))=11.82, p<0.000 with an R2 of 0.12. Participants predicted 3D MOT score is equal to 1.05 –0.01 KD + 0.07 DR + 0.23 COOR, where KDT is measured in seconds, DR is measured in units between 0–5, and COOR is measured as 1=successful, 0?= not successful. The analysis shows that KD (Beta=–0.01, p?< 0.000), DR (Beta=0.07, p<0.02), and COOR (Beta=0.23, p<0.03), were significant predictors of 3D MOT scores. Conclusions Results suggest that King Devick Test, Delayed Recall, and Coordination tests share predictive validity of the 3D MOT in an athletic population between the ages of 6-29 at baseline. Future studies should examine these relationships post-injury and through concussion recovery. This could provide valuable information to better inform clinicians responsible for making Return to Play determinations. Competing interests None.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".