Review of brief cognitive tests for patients with suspected dementia
Bibliographic record
Abstract
BACKGROUND: As the population ages, it is increasingly important to use effective short cognitive tests for suspected dementia. We aimed to review systematically brief cognitive tests for suspected dementia and report on their validation in different settings, to help clinicians choose rapid and appropriate tests. METHODS: Electronic search for face-to-face sensitive and specific cognitive tests for people with suspected dementia, taking ≤ 20 minutes, providing quantitative psychometric data. RESULTS: 22 tests fitted criteria. Mini-Mental State Examination (MMSE) and Hopkins Verbal Learning Test (HVLT) had good psychometric properties in primary care. In the secondary care settings, MMSE has considerable data but lacks sensitivity. 6-Item Cognitive Impairment Test (6CIT), Brief Alzheimer's Screen, HVLT, and 7 Minute Screen have good properties for detecting dementia but need further validation. Addenbrooke's Cognitive Examination (ACE) and Montreal Cognitive Assessment are effective to detect dementia with Parkinson's disease and Addenbrooke's Cognitive Examination-Revised (ACE-R) is useful for all dementias when shorter tests are inconclusive. Rowland Universal Dementia Assessment scale (RUDAS) is useful when literacy is low. Tests such as Test for Early Detection of Dementia, Test Your Memory, Cognitive Assessment Screening Test (CAST) and the recently developed ACE-III show promise but need validation in different settings, populations, and dementia subtypes. Validation of tests such as 6CIT, Abbreviated Mental Test is also needed for dementia screening in acute hospital settings. CONCLUSIONS: Practitioners should use tests as appropriate to the setting and individual patient. More validation of available tests is needed rather than development of new ones.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".