King-devick (kd) test as a rinkside tool for concussion assessment
Bibliographic record
Abstract
Objective King-Devick test as a rinkside tool for concussion diagnosis. Design KD was administered to hockey players immediately after removal from the game with a suspected concussion. Results were compared to baseline. Concussion was suspected with slowing by >5.2 sec.1 Setting Hockey games. Participants Hockey players (male/female) – school-based hockey academy and a Canadian junior hockey team Interventions Athletic trainers were trained in the use of KD and obtained baseline KD times for players. AT’s administered the KD test to hockey players immediately after removal from the game with a suspected concussion. Main outcome measures KD time post-injury was compared to the KD time baseline. Results During the 2015–16 season, KD testing was collected on players with suspected concussion (42 concussions identified out of 148 players). Of the 42 concussions, 13 had KD sideline testing done immediately post-injury; 8/13 demonstrated >5.2 sec slowing in their KD baseline scores. All were further evaluated with a comprehensive concussion assessment protocol that included symptom scoring-balance assessments-cognitive testing. Concussion was confirmed with this diagnostic approach in 8/8 players with KD times slowed by more than 5.2 sec. Abstarct 212 Table 1 King-Devick Sideline Assessment for Concussion: Diagnostic Difference >5.2 sec from Baseline AGE GENDER SPORT Baseline Sideline ΔKD 14yr Male Hockey 53.08 61.08 8.0 sec 14yr Male Hockey 30.09 42.81 12.72 15yr Female Hockey 42.5 53.5 11.0 15yr Female Hockey 37.0 54.0 23.0 17yr Female Hockey 46.5 54.05 7.55 18yr Male Hockey 36.88 43.35 6.97 19yr Male Hockey 36.01 61.0 24.99 20yr Male Hockey 39.02 47.08 8.06 Conclusions An ideal concussion sideline diagnostic tool should be inexpensive, portable, reproducible, fatigue-tolerant, resistant to test-retest learning and suitable for non-medical personnel.23The King-Devick test, that assesses saccadic eye movements, has these characteristics. It can be administered in less than 2-minutes. It has been reported that a post-injury slowing of KD times >5.2 seconds is diagnostic of concussion.4Sideline/rinkside KD testing with > 5.2 sec slowing compared to baseline results accurately identified concussion with 100% accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".