Implementing the GSA KAER Toolkit to Enhance Detection and Management of Cognitive Impairment in Older Adults
Bibliographic record
Abstract
Abstract Background Cognitive impairment and dementia affect older adults in the US but often go undiagnosed in primary care due to time constraints, stigma, and lack of standardized protocols. The Gerontological Society of America’s KAER (Kickstart, Assess, Evaluate, Refer) Toolkit offers a structured approach to improve early identification and management. Methods This quality improvement project was conducted in a primary care brain clinic from September 2023 to June 2025 for patients aged ≥65 years. Cognitive screening was integrated into routine visits. Patients with concerns identified in the Kickstart phase underwent detailed history-taking, functional assessment, and standardized testing, including the Montreal Cognitive Assessment and Patient Health Questionnaire-9. Reversible causes were investigated, and individualized care plans were developed with referrals to subspecialty care and community resources. Data collected included prevalence, comorbidities, functional status, and referral patterns. Results Among 41 patients screened, 80% had cognitive impairment, most aged 66–75 years. Women were more likely to have dementia; men more often had mild cognitive impairment. Patients comprised 42% African American, 27% Hispanic, and 27% White. Depression (83%) and anxiety (67%) were common, along with comorbidities such as hypertension (23%) and chronic kidney disease (23%). Two patients had positive ApoE genotypes with MRI-confirmed pathology. Conclusions KAER Toolkit implementation identified high rates of cognitive impairment and comorbidities, supporting its user-friendly design to improve early detection, targeted interventions, and coordinated care in older adults.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".