Cognitive Tests to Detect Dementia
Bibliographic record
Abstract
IMPORTANCE: Dementia is a global public health problem. The Mini-Mental State Examination (MMSE) is a proprietary instrument for detecting dementia, but many other tests are also available. OBJECTIVE: To evaluate the diagnostic performance of all cognitive tests for the detection of dementia. DATA SOURCES: Literature searches were performed on the list of dementia screening tests in MEDLINE, EMBASE, and PsychoINFO from the earliest available dates stated in the individual databases until September 1, 2014. Because Google Scholar searches literature with a combined ranking algorithm on citation counts and keywords in each article, our literature search was extended to Google Scholar with individual test names and dementia screening as a supplementary search. STUDY SELECTION: Studies were eligible if participants were interviewed face to face with respective screening tests, and findings were compared with criterion standard diagnostic criteria for dementia. Bivariate random-effects models were used, and the area under the summary receiver-operating characteristic curve was used to present the overall performance. MAIN OUTCOMES AND MEASURES: Sensitivity, specificity, and positive and negative likelihood ratios were the main outcomes. RESULTS: Eleven screening tests were identified among 149 studies with more than 49,000 participants. Most studies used the MMSE (n = 102) and included 10,263 patients with dementia. The combined sensitivity and specificity for detection of dementia were 0.81 (95% CI, 0.78-0.84) and 0.89 (95% CI, 0.87-0.91), respectively. Among the other 10 tests, the Mini-Cog test and Addenbrooke's Cognitive Examination-Revised (ACE-R) had the best diagnostic performances, which were comparable to that of the MMSE (Mini-Cog, 0.91 sensitivity and 0.86 specificity; ACE-R, 0.92 sensitivity and 0.89 specificity). Subgroup analysis revealed that only the Montreal Cognitive Assessment had comparable performance to the MMSE on detection of mild cognitive impairment with 0.89 sensitivity and 0.75 specificity. CONCLUSIONS AND RELEVANCE: Besides the MMSE, there are many other tests with comparable diagnostic performance for detecting dementia. The Mini-Cog test and the ACE-R are the best alternative screening tests for dementia, and the Montreal Cognitive Assessment is the best alternative for mild cognitive impairment.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".