Concussion diagnosis: the king-devick test in the canadian football league
Bibliographic record
Abstract
Objective To conduct a sensitivity analysis of the King-Devick (K-D) test in professional football. Design Prospective cohort. Setting Professional football. Participants: 269 professional football players from the Canadian Football League (CFL). There were 24 concussions to analyse. Intervention The K-D test was added to the existing CFL concussion protocol (medical and SCAT3). All participants completed K-D assessments at baseline, at the time of injury/concussion (TOI), and at medical clearance prior to return to play (RTP). 20 controls were re-tested post-baseline. Outcome measures K-D scores were analysed to construct a sensitivity analysis. Main results TOI K-D results were significantly higher (mean=50.21, range: 35.4–107.4) than baseline K-D results (mean=44.3, range 28.4–66.4; p<0.01). TOI K-D results yielded 94% sensitivity and 80% specificity for diagnosing concussions. Four groups emerged from the TOI data. In Group 1, 4/4 were asymptomatic within 24 hours and scores were better (lower) than baseline; Group 2 were asymptomatic within 72 hours and 8/9 had abnormal (poorer) scores; Group 3 were asymptomatic within 11 days and 5/5 had abnormal scores. Group 4 were symptomatic by season’s end and 4/4 had abnormal scores. 18/18 players who RTP had better K-D scores than baseline prior to RTP. Conclusions The K-D test proved to be useful for concussion diagnosis. Interestingly, the players in Group 1 had normal TOI K-D scores and were asymptomatic in <24 hours. More research is needed and the CFL will continue this next season. Competing interests None.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".