INNOVATION IN AGING & ACQUIRED BRAIN INJURY REHABILITATION: THE POTENTIAL OF MIXED REALITY TECHNOLOGIES
Bibliographic record
Abstract
Abstract Background Every 21 seconds an American experiences a Traumatic Brain Injury (TBI), and every 40 seconds, another endures a stroke. These events fall under Acquired Brain Injuries (ABI) with prevalence rates impacted by an aging population. ABI can lead to various physical, cognitive, and mental impairments, emphasizing the need for innovative rehabilitation strategies, including those specific to older adults. Mixed Reality (MR) technologies offer potential in enhancing ABI rehabilitation, yet face challenges such as methodological inconsistencies, differing clinical populations, and exaggerated efficacy claims. Objectives 1) Review MR’s role in ABI rehabilitation among older adults, assessing the implications of aging, clinical, and technological considerations. 2) Present ongoing MR research at the Glenrose Rehabilitation Hospital (GRH, Edmonton, Canada). Methods 1) A systematic review following PRISMA guidelines was performed across seven databases, with two independent reviewers analyzing the data. The analysis emphasized clinical objectives, MR systems, levels of evidence, and technology readiness levels. 2) QR codes that link to videos will highlight the ongoing research and development of MR-delivered ABI rehabilitation at the GRH. Results Twenty-six studies met the inclusion criteria, totalling 453 subjects with ABI (mean age: 60 ± 5.34 years). MR applications mainly targeted upper limb motor rehabilitation, revealing an overall low level of evidence and a median technology readiness level of 6 (prototypes tested in relevant environments). Conclusion Despite existing variability and technological challenges, the promising results stress the importance of ongoing research and innovation in MR rehabilitation. The GRH stands as a key research hub, actively advancing this field.
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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 | 0.032 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".