How can we facilitate adoption and scale for early detection of Alzheimer’s disease? Insights from implementing a community‐based screening program using cognitive assessments and RetiSpec screening in Ontario, Canada
Bibliographic record
Abstract
Abstract Background A critical need to increase Alzheimer’s disease (AD) screening exists given rising incidence, new disease‐modifying treatments, and ill‐equipped primary care settings. This study assessed the feasibility of a novel, community‐based AD screening program to increase cognitive and retinal‐based assessments. Methods An observational study, supported by the Davos Alzheimer’s Collaborative, assessed the utility of leveraging community‐based settings to increase rates of cognitive assessment (conducted by Alzheimer Society (AS) social workers [SWs]) and RetiSpec’s AI‐based eye test in optometry settings to detect biologic signatures of AD (plus participant survey). Eligible participants were adults aged ≥55 years with self‐reported memory concerns. Primary and secondary endpoints were to increase cognitive assessment rates and have 10% of assessments originate from optometry, respectively. A utilization‐focused evaluation explored risks, benefits, facilitators, and barriers to these settings from frontline providers’ perspectives. Results N = 916 individuals were screened (60.2% from optometry) with N = 134 participants enrolled (age: 74.3 years±7.0; 63.7% female; 39.5% minority groups). N = 124 participants received cognitive assessments: 118 Montreal Cognitive Assessment (mean score: 24.7±3.7), 124 Boston Naming Test (13.3±2.0), 6 Mini‐Mental State Exam (18.7±5.7) and 6 Clock Drawing Test (1.2±1.2). N = 120 participants discussed their results with a clinician (37.9% with the NP). N = 25 dementia specialist referrals were made, N = 52 received an AD‐related diagnosis, along with medication prescription (N = 5), further medical investigation (N = 30) and clinical monitoring (N = 27). N = 99 underwent a RetiSpec scan (N = 65 predicted positive). Surveys (N = 71) demonstrated a positive scan experience (4.2±1.3/5) and unanimous interest in sharing results with a PCP (5.0±0.0/5). Endpoint 1 was met with 13.8 assessments/month completed versus 1.8/month in the pre‐study period. Endpoint 2 was met with 29.0% of assessments originating from optometry. Evaluation findings indicate that screening programs should consider: promoting brain health awareness in community settings; utilizing clinicians with AD training for screening and follow‐up activities; leveraging optometry clinics and AS’s to enable broad access to screening; and adopting technology that leverages existing infrastructure (i.e., RetiSpec’s eye test) to enable scalable increases to AD screening. Conclusions Utilizing community‐based settings, including optometry clinics for RetiSpec screening and AS SWs for cognitive screening, led to meaningful improvements in early detection and subsequent medical care.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".