Brief cognitive screening instruments: an update
Bibliographic record
Abstract
OBJECTIVE: To review the recent literature on cognitive screening with a focus on brief screening methods in primary care as well as geriatric services. DESIGN: The Medline search engine was utilized using the keyword search terms 'cognitive screening', 'cognitive assessment', and 'dementia screening' limiting articles to those published in English since 1998. RESULTS: 679 abstracts were retrieved. Articles focusing on attitudes toward cognitive screening, current screening practices, promising new instruments and more recent updates contributing significant information on established instruments were retrieved and incorporated into this review. Reference lists were reviewed for relevant contributing articles. Instruments recommended from previous reviews of cognitive screening and those identified in surveys as most frequently used in primary care and geriatric settings were emphasized in this review. CONCLUSIONS: Dementia remains under-diagnosed in the elderly population. Despite significant limitations, the Mini Mental State Exam remains the most frequently used cognitive screening instrument. Its best value in the community and primary care appears to be for the purpose of ruling out a diagnosis of dementia. Instruments such as the Mini-Cog, Memory Impairment Screen (MIS), and the General Practitioner Assessment of Cognition (GPCOG) have consistently been recognized for utility in primary care. The clock drawing test (CDT) and newer instruments such as the Montreal Cognitive Assessment (MoCA) and the Rowland Universal Dementia Assessment Scale (RUDAS) are gaining credibility due to improvements in sensitivity, addressing frontal/executive functioning, and decreasing susceptibility to cultural and educational biases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".