Long-term reliability of ImPACT in professional ice hockey
Bibliographic record
Abstract
OBJECTIVE: This study sought to assess the test-retest reliability of Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) across 2-4 year time intervals and evaluate the utility of a newly proposed two-factor (Speed/Memory) model of ImPACT across multiple language versions. METHOD: Test-retest data were collected from non-concussed National Hockey League (NHL) players across 2-, 3-, and 4-year time intervals. The two-factor model was examined using different language versions (English, French, Czech, Swedish) of the test using a one-year interval, and across 2-4 year intervals using the English version of the test. RESULTS: The two-factor Speed index improved reliability across multiple language versions of ImPACT. The Memory factor also improved but reliability remained below the traditional cutoff of .70 for use in clinical decision-making. ImPACT reliabilities remained low (below .70) regardless of whether the four-composite or the two-factor model was used across 2-, 3-, and 4-year time intervals. CONCLUSIONS: The two-factor approach increased ImPACT's one-year reliability over the traditional four-composite model among NHL players. The increased stability in test scores improves the test's ability to detect cognitive changes following injury, which increases the diagnostic utility of the test and allows for better return to play decision-making by reducing the risk of exposing an athlete to additional trauma while the brain may be at a heightened vulnerability to such trauma. Although the Speed Index increases the clinical utility of the test, the stability of the Memory index remains low. Irrespective of whether the two-factor or traditional four-composite approach is used, these data suggest that new baselines should occur on a yearly basis in order to maximize clinical utility.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".