Characteristics of the King-Devick test in the assessment of concussed patients in the subacute and later stages after injury
Bibliographic record
Abstract
Although the King-Devick (K-D) test has been used frequently in assessing sports related concussion early after injury, its characteristics over time after injury and in patients with prolonged persistent symptoms are unknown. The purpose of this paper was to: evaluate the ability of the K-D Test to distinguish patients seen early after concussion from those with symptoms persisting more than 3 months compared to controls, assess changes in the K-D test times over time after concussion, and determine the relationship of K-D times to the Stroop Color and Word Test scores. We performed cross-sectional comparisons of patients with recent concussive brain injury (acute group) and those with symptoms persisting more than 3 months to healthy controls on the K-D test, the Sports Concussion Assessment Tool 3 (SCAT3), and the Stroop Color and Word Test. Longitudinal comparisons of the acute group over time within the first month after injury were also made. Post-concussive syndrome (PCS) patients had significantly higher K-D times compared to controls (p = 0.01), while the acute group did not differ from controls(p = 0.33). K-D times at the second visit for the acute group were similar to those of controls (54.7 vs. 49.6, p = 0.31). While SCAT3 scores improved over time in the acute group, the K-D scores did not change between the first and second visit (55.2 vs. 54.7, p = 0.94). K-D scores correlated significantly with the Stroop scores for all three participant groups. The K-D test is likely useful very early after concussion in conjunction with baseline scores, and while scores in PCS patients remain elevated, they can be confounded by factors such as pre-morbid depression and medication use. High correlations with Stroop scores also suggest that performance on the K-D test can by proxy provide additional insight about cognitive function and predict performance on more cognitively demanding tasks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".