The Utility of the King-Devick Test in Evaluating Professional Ice Hockey Players with Suspected Concussion
Bibliographic record
Abstract
Abstract Purpose The King-Devick (KD) is a measure of rapid number naming used in the evaluation of sports-related concussion (SRC). Recent data from the Canadian Football League and Rugby Union suggest that the KD should not be used as a stand-alone measure of SRC. The purpose of this study was to examine the diagnostic utility of the KD in professional ice hockey players. Methods NHL players who were suspected of having a concussion were evaluated with the KD and either the SCAT3 or the SCAT5. Players who were evaluated and not diagnosed with concussion served as Active controls. A small group of non-Active control players was also tested twice and was included in the present study for comparison. Results 1605 players were evaluated with the KD at baseline. Of these, 53 were diagnosed with concussion, 76 were Active controls, and 11 were non-Active controls. Concussed players revealed a decline in performance from baseline to acute evaluation, t(52)=3.05, p<.01, d=.42 while Active controls significantly improved, t(75)=2.05, p<.05, d=.24. No significant change between baseline and acute testing was observed for non-Active controls. Using a cut score of any decline in performance from baseline to suspected injury evaluation yielded Sensitivity=64%, Specificity=61%, Positive Predictive Value=53% and Negative Predictive Value=71%. Conclusion Our data are consistent with previous studies suggesting that while the KD is useful in differentiating concussed and not concussed athletes acutely, the relatively low predictive values indicate that a decline in KD performance should not be used as a standalone measure to diagnose concussion.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".