Sideline Concussion Assessment: The King-Devick Test in Canadian Professional Football
Bibliographic record
Abstract
Sideline assessment tools are an important component of concussion evaluations. To date, there has been little data evaluating the clinical utility of these tests in professional football. The purpose of this study was to evaluate the clinical utility of the King-Devick (K-D) test in evaluating concussions in professional football players. Baseline data was collected over two consecutive seasons in the Canadian Football League as part of a comprehensive medical baseline evaluation. A pilot study with the K-D test began in 2015 with 306 participants and the next year (2016) there were 917 participants. In addition, a sample of 64 participants completed testing after physical exertion (practice or game). Participants with concussion demonstrated significantly higher (slower) results compared with baseline and the exercise group (F[2,211] = 5.94; p = 0.003). The data revealed a specificity of 84% and sensitivity of 62% for our sample. Reliability from season to season was good (intraclass correlation coefficient [ICC] 2,1 = 0.88; 95% confidence interval [CI]: 0.83, 0.91). On average, participants improved performances by a mean of 1.9 sec (range, -26.6 to 23.8) in subsequent years. High reliability was attained in the exercise group. (ICC2,1 = 0.93; 95% CI: 0.89, 0.96). The K-D test presents as a reliable measure although sensitivity and specificity data from our sample indicate it should be used in conjunction with other measures for diagnosing concussion. Further research is required to identify stability of results over multiple usages.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".