Evaluating Cognitive Screening Tools for Older Adults in Ambulatory Care Settings
Bibliographic record
Abstract
Abstract Implementation of the Age-Friendly Health Systems 4Ms framework ensures that assessing and acting on What Matters, Medication, Mentation, and Mobility is included for all patients aged 65 and older. In ambulatory settings, patients in this age group are often assessed annually for dementia using brief cognitive screening tools to screen for Alzheimer’s Disease/Related Dementias (ADRD). Providers often report that older adults are hesitant to engage in the screening, frequently stating that they do not perceive any memory issues. Additionally, controversy exists on the benefits for screening all older adults. The purpose of this literature review is to present: (a) current instruments used for cognitive screening such as the Mini-Cog, the Eight-item Informant Interview to Differentiate Aging and Dementia (AD8®), Memory Impairment Screen (MIS), and Montreal Cognitive Assessment (MoCA); (b) barriers to dementia screening from both provider’s and patients’ perspectives; and (c) the ongoing controversy surrounding population screening for dementia in the ambulatory care setting. This review will include a comparison of these instruments in terms of time to complete, acceptability, specificity, and sensitivity. Current utilization and recommendations for these screening tools in ambulatory care settings will be reported. Strategies for implementing these assessments in ambulatory care, where patients often expect short and succinct appointments, will also be presented.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".