Brief cognitive screening instruments for early detection of Alzheimer’s disease: a systematic review
Bibliographic record
Abstract
Abstract Objectives The objective of this systematic review was (1) to give an overview of the available short screening instruments for the early detection of Alzheimer’s disease (AD) and (2) to review the psychometric properties of these instruments. Methods First, a systematic search of titles and abstracts of PubMed and Web of Science was conducted between February and July 2015 and updated in April 2016 and May 2018. Only papers written in English or Dutch were considered. All full-text papers about cognitive screening instruments for the early detection of AD were included, resulting in the identification of 38 pencil and paper tests and 12 computer tests. In a second step, the psychometric quality of these instruments was evaluated. Therefore, the same databases were searched again to identify papers that described the psychometric properties of the instruments meanwhile applying diagnostic criteria for the diagnostic groups included. Results Out of 1454 papers, 96 clearly discussed the psychometric properties of the instruments. Eighty-nine papers discussed pencil and paper tests of which 80 were validated in a memory clinic setting. Based on the number of studies (31 articles) and the sensitivity (84%) and specificity (74%) values, the Montreal Cognitive Assessment (MoCA) seems to be a promising (pencil and paper) screening test for memory clinic testing as well as for population screening. Regarding computer tests, validation studies were only available for 7 out of 12 tests. Conclusions A large number of screening tests for AD are available. However, most tests are only validated in a memory clinic setting and description of the psychometric properties of the instruments is limited. Especially, computer tests require further research. The MoCA is a promising instrument, but the specificity to detect early AD is rather low.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.039 | 0.019 |
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; both teacher heads agree on what is shown here.
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".