Initial validation of SENIC: a cognitive test for assessing concussion in team sports
Bibliographic record
Abstract
INTRODUCTION: Concussions present a significant public health concern, with an estimated 1.6 to 3 million sport-related cases reported annually in the United States alone. Athletes are particularly vulnerable due to repeated exposure to high-risk situations. We wish to validate a novel assessment tool designed to evaluate cognitive functioning through a sport-specific, decision-based task. METHOD: This study introduces SENIC (ENgaging and Immersive Cognitive Simulation), a dynamic, context-sensitive cognition task developed collaboratively with athletes and stakeholders. SENIC integrates ecological validity by contextualizing cognitive tasks within the athlete's sport. This approach offers an integrated view of cognition, as opposed to traditional methods that assess cognitive functions independently. Reaction time, a behaviorally linked indicator, serves in this study as a measure of information processing efficiency. Ninety-six athletes without current or recent concussion completed SENIC and the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) battery. Construct validity was examined using a multitrait-multimethod matrix (MTMM) approach. RESULTS: The MTMM revealed correlations between SENIC's detection time and ImPACT's reaction time, ImPACT's visuomotor speed, and ImPACT's visual memory, providing preliminary evidence for convergent validity. CONCLUSION: Our study proposes an innovative neurocognitive assessment approach that combines external validity with dynamic cognition. SENIC seems promising in providing a contextually relevant evaluation of cognitive functioning in athletes at risk of concussion.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".