The utility of the King-Devick test as a sideline assessment tool for sport-related concussions: a narrative review.
Bibliographic record
Abstract
OBJECTIVE: The objective of this paper is to review existing literature surrounding the utility of the King-Devick test which is a commonly used sideline assessment tool for sport-related concussions. METHODS: , 2015. RESULTS: This search strategy yielded 27 articles from aforementioned databases. Further searching in The Cochrane Library with King-Devick AND Concuss* search terms yielded one additional article, summing a total of 28 articles. After removal of duplicates and implementation of the inclusion/exclusion criteria, 8 articles for extensively reviewed. CONCLUSION: This narrative review suggests that the King-Devick test is an efficient sideline assessment tool for sport-related concussions. However, we recommend that the King-Devick should be used as a sideline screening tool, not a concussion diagnosis tool at this time. A proper baseline time including multiple tests may be recommended to negate the learning affect and to have a reliable baseline in which to measure from for future reference. A three second difference appears appropriate to identify the possibility of concussion and to remove an athlete from play. At this time, the athlete should be monitored and further evaluated as symptoms are sometimes delayed. We suggest that further research may be useful to better determine the efficacy of the K-D test in detecting concussions across a broader range of athletes and sports. We also suggest further research may investigate the K-D test a potential return-to-play tool for clinicians and medical personnel.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".