Beyond the Lab: Scaling Wearables for Real‐World Impact through Partnerships and Implementation
Bibliographic record
Abstract
Despite the advancement of wearable technologies, widespread adoption and implementation of these potentially transformative technologies to support aging individuals, including those with mild cognitive impairment (MCI), Alzheimer's disease, and related dementias (ADRD), has yet to be achieved. This barrier to adoption has prevented us from fully realizing the potential transformative impact wearables could have - both in the delivery of compassionate and accessible dementia care and in improving outcomes for aging individuals. This session will share lessons learned from the Ontario Brain Institute's journey to co-design and launch a program called CORTEX (Community-led Real-world neuroTech Experience) that empowers community groups and people with lived experience in testing, implementing, and scaling wearable technology. We will share how the initial concept for CORTEX emerged, how the program was initially co-designed with community groups and people with lived experience, and the learnings from the first pilots conducted in partnership with patient groups. Finally, we will show how the learnings informed the launch of our flagship CORTEX initiative, the Canadian Dementia Registry, which was co-created in partnership with the Alzheimer Society of Ontario. This registry allows us to (1) understand the earliest stage of the dementia journey diagnosis (including how conducting cognitive assessments in the community, on behalf of primary care practitioners fills an early detection gap), (2) test neurotechnologies in diverse populations and compare them to clinically-relevant scales (e.g., MoCA), and (3) look at how these technologies can fit into the care pathway in local communities so that we can increase access to the right clinicians and care they need and deserve.
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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 | 0.046 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".