DIGITAL APPROACHES TO ROUTINE COGNITIVE SCREENING FOR OLDER ADULTS IN PRIMARY CARE SETTINGS
Bibliographic record
Abstract
Abstract Cognitive screening remains under-utilized for older adults in primary care due to time constraints and limitations of existing measures. Well-validated digital assessments could potentially increase screening efficiency and accuracy. We compared two new digital cognitive tools with the Montreal Cognitive Assessment (MoCA) in a sample of 32 dementia-free older adults (ages 55-85) completing annual follow-up visits with a primary care provider (PCP). Participants self-administered the Boston Online Cognitive Assessment (BOCA), an online measure with alternate forms, twice prior to (1-4 weeks before and day of) an upcoming PCP follow-up visit. At their visit, they completed the Digital Clock and Recall (Linus Health DCRTM), a 5-minute, provider-administered tablet-based measure. Finally, they completed the MoCA with a research coordinator or behavioral health staff at the clinic. Five PCPs aided in protocol development and participated in data collection. The sample is currently 54% female and 81% White. Test-retest reliability for the BOCA was excellent (r =.81). BOCA score (time 1) was correlated with scores for the DCR and MoCA at the p <.05 level. The association between the DCR and MoCA approached significance. On an exit survey, 79% of participants said that they would prefer to do cognitive screening at home before their appointment, compared to in the clinic (21%). These preliminary data replicate excellent test re-test reliability for the BOCA and demonstrate good convergent validity between the BOCA and two provider-administered screening measures. Next, we will compare test accuracies to detect impairment and their associations with demographics variables.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".