The Utility of the King-Devick Test in Evaluating Professional Ice Hockey Players With Suspected Concussion
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to (1) examine the diagnostic utility of the King-Devick (KD) in professional ice hockey players and (2) determine whether the KD provides unique variance in predicting a diagnosis of concussion when given in combination with the SCAT-5. STUDY DESIGN: Cohort Study. SETTING: Primary care. PATIENTS/PARTICIPANTS: Professional ice hockey players. INDEPENDENT VARIABLES: Diagnosis versus no diagnosis of concussion. MAIN OUTCOME MEASURES: King-Devick and SCAT-5 component scores. METHODS: In part 1, players suspected of having a concussion were evaluated at baseline and acutely with the KD and either the SCAT-3 or the SCAT-5. Players evaluated and not diagnosed with concussion served as active controls. In part 2, a separate group of players suspected of having a concussion was evaluated acutely with both the KD and SCAT-5. RESULTS: In part 1 of this study, 53 concussed players declined in performance on the KD from baseline to acute evaluation, whereas the performance of 76 active controls improved significantly. In part 2 of the study, 75 players were diagnosed with concussion and compared with 80 active controls who were evaluated and not diagnosed with concussion. Concussed players revealed a decline in KD performance from baseline to acute evaluation when compared with controls. However, the KD did not account for significant unique variance in predicting a diagnosis of concussion after accounting for SCAT-5 data. CONCLUSIONS: The KD is useful in differentiating concussed and not concussed athletes acutely, but the KD does not seem to add additional diagnostic value over and above the SCAT-5.
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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 | 0.000 |
| 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".