Digital Assessment Approaches to Overcome Barriers to Cognitive Screening and Monitoring for Older Adults in Primary Care Settings
Bibliographic record
Abstract
Abstract Background Routine cognitive screening for older adults in primary care could improve AD early detection and streamline referrals for treatment or clinical trials. Digital assessments, especially when self‐administered online, can overcome time barriers to cognitive screening in primary care settings and are conducive to repeat testing for disease monitoring and the evaluation of treatment outcomes. We report preliminary data on the feasibility and acceptability of three digital screening approaches for older adults completing annual follow‐up visits with a primary care provider (PCP) Methods Cognitive screening approaches included: 1) remote online screening with the Boston Online Cognitive Assessment (BOCA) 1‐4 weeks prior to the PCP appointment, 2) self‐administered BOCA in the waiting room before or after the appointment, and 3) provider‐administered screening during the appointment using the Digital Clock and Recall (Linus Health DCRTM). Participants also completed a brief in‐clinic cognitive health consultation that included the Montreal Cognitive Assessment (MoCA), test feedback, and recommendations. Five PCPs aided in protocol development and participated in data collection. Potential participants were identified using EMR. Exclusion criteria: dementia or other neurological disease, score of <13 on the t‐MoCA. Results 48 older adults ages 55‐85 were screened, 34 enrolled, and 4 withdrew. The sample is 54% female, 84% White, education M = 14.8 years. Self‐administration of BOCA in waiting room was discontinued early due to space, time, and coordination challenges. Participants instead completed a second at‐home administration of the BOCA on the day of their appointment. Completion rates were 81% for advance BOCA, 62% for same‐day BOCA, and 86% for in‐clinic DCR. Higher priority health issues and not returning for the consultation visit were primary reasons for withdrawal. Most participants reported a preference for screening at home compared to in‐clinic. Conclusions At‐home, self‐administered assessment online and in‐clinic, provider‐administered assessment on a tablet both show preliminary evidence of feasibility and acceptability for cognitive screening and monitoring use in primary 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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".