Examining Technological Applications Used for the Cognitive Assessment and Rehabilitation of Concussed Individuals: A Rapid Review
Bibliographic record
Abstract
The use of technological applications for cognitive assessment and rehabilitation is growing, yet tools specifically targeting cognition in concussed individuals remain underexplored. This rapid review examined technologies used for cognitive assessment and/or rehabilitation following concussion. Specific objectives were to identify (1) cognitive domains targeted, (2) participant populations recruited, (3) quality of assessment or therapeutic impact, and (4) user involvement in application design. A structured search across three databases yielded 16 articles analyzing 21 applications. Four (25%) focused primarily on cognition, while the remainder addressed multiple domains. Most applications assessed cognition, and study populations frequently included athletes and military members/veterans. Only two (12.5%) studies reported user feedback on application design. Findings suggest a need for broader requirements of concussed civilians to improve representativeness, and for future research to prioritize the development of applications targeting cognitive rehabilitation in concussed populations.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".