Comparative accuracy of screening instruments for Alzheimer’s disease: Systematic review and meta-analysis
Bibliographic record
Abstract
Background: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline. Early detection and accurate screening of AD are crucial for timely interventions and improved patient outcomes. Various screening instruments have been developed to aid in the identification of individuals at risk of AD. However, the comparative accuracy of these instruments has not been thoroughly assessed. Aim: This study aims to evaluate and compare the accuracy of different screening instruments used for the detection of AD. Method: A PRISMA selection was used to identify studies across electronic database such as PubMed and Google scholar from up until February 4, 2023.A total of 5 studies evaluating neuropsychological assessment such as, Mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA), and clinical dementia rate (CDR) between patients with AD, mild cognitive impairment (MCI) and healthy control (HC). Meta-analysis was performed by Rev-Man 5.4. Result: The studies included a total number of 1,177 individuals, 398 were in the AD group,409 in MCI and 370 in HC group. The cognitive function assessed by the meta-analysis revealed AD with lesser MMSE (P < 0.00001), MoCA (P < 0.00001), when compared to MCI. But CDR score was decrease with MCI (P < 0.00001). In addition, AD showed a lesser MSSE (P < 0.00001), CDR (P < 0.00001), and MoCA (P < 0.00001), scores when compared to HC. Conclusion: The findings indicate that individuals with AD exhibit lower scores in MMSE, MoCA, and CDR compared to those with MCI and HC.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".