3.5 SENIC: a sport concussion assessment and empowerment tool based on serious gaming
Bibliographic record
Abstract
Objective To examine the reliability and learning effect of an assessment tool based on serious gaming (SENIC) for sport-related concussions. A secondary objective was to collect preliminary data on responsiveness following a concussion. Researchers hypothesized that no learning effect would be observed and that performance would be impaired following a concussion. Design Longitudinal study. Performance at SENIC was assessed three times during athletes’ season. Following a concussion, athletes were also invited to perform the test within a 72-hour period. Setting and Participants One hundred healthy soccer varsity athletes (34 males, 66 females) aged between 13 and 26 years old (M = 17.68, SD = 3.03). All had normal or corrected-to-normal vision and reported no persistent symptoms from a past concussion. Outcome Measures Performance at SENIC included the percentage of correct detection and median detection time for specific events in sport-specific video sequences. Main Results Results revealed no significant difference across test sessions regarding the percentage of detection (M = 71.08, 73.01, and 72.07%, respectively, p = .150, ƒ = .078) and detection time at SENIC (M = 443, 422, and 446 ms, respectively, p = .152, ƒ = .079). Preliminary data on six concussed athletes showed that most of them exhibited a slower response time (4/6, = -116 ms) and a lower percentage of detection (5/6, = – 4.74%) compared to baseline. Conclusions The present study suggests that SENIC has the potential to help better identify concussion-related cognitive impairments while avoiding the learning effects associated with cognitive testing.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".