Test–Retest Reliability of Remote ImPACT Administration
Bibliographic record
Abstract
OBJECTIVE: To evaluate the performance and test-retest reliability obtained when administering a computerized baseline neurocognitive exam to NCAA Division I student-athletes in a controlled laboratory setting versus an uncontrolled remote location. METHOD: A sample of 129 (female = 100) Division I student-athletes completed Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) pre-season assessments for two distinct and respective sports seasons in a controlled laboratory environment and an uncontrolled remote environment. Depending on the environment, participants were given verbal (controlled) or written (uncontrolled) guidelines for taking the test. RESULTS: Multivariate repeated-measures ANOVA's determined that there were no within-subject differences between testing environments on ImPACT composite scores and cognitive efficiency index (CEI). The Chi-square test did not find any significant differences in impulse control or the number of invalid test scores, as determined by ImPACT, between environments. Intraclass correlations found the ImPACT subtest scores to range in test-retest reliability across testing environments, demonstrating moderate (verbal memory composite, r = 0.46; visual memory composite, r = 0.64; reaction time, r = 0.61; impulse control, r = 0.52; and CEI, r = 0.61) and good (visual motor composite, r = 0.77) test-retest reliability. CONCLUSIONS: Results indicate that ImPACT is reliable between controlled and uncontrolled testing environments. This further suggests that ImPACT can be administered in a remote environment, pending specific adherence to testing instructions, or in the event of social distancing or isolation policies.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".