Diagnostic Sensitivity and Specificity of Cognitive Tests for Mild Cognitive Impairment and Alzheimer’s Disease in Patients with Down Syndrome: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Improved health care for people with Down syndrome (DS) has resulted in an increase in their life expectancy therefore increasing comorbidities associated with age-related problems in this population, the most frequent being Alzheimer's disease (AD). To date, several cognitive tests have been developed to evaluate cognitive changes related to the development of mild cognitive impairment (MCI) and AD in people with DS. OBJECTIVE: Identify and evaluate available cognitive tests for the diagnosis of MCI and AD in people with DS. METHODS: A systematic search of the Pubmed and PsycInfo databases was performed to identify articles published from January 1, 2000 and July 1, 2022. Keysearch terms were DS, AD or MCI, cognition, and assessment. Relevant studies assessing the diagnostic accuracy of cognitive tests for AD or MCI with standard clinical evaluation were extracted. Risk of bias was assessed using the QUADAS 2. RESULTS: We identified 15 batteries, 2 intelligence scales, 14 memory tests, 11 executive, functioning tests, 11 motor and visuospatial functioning tests, 5 language tests, 3 attention tests, and 2 orientation tests. Analysis showed that the CAMCOG-DS present a fair to excellent diagnostic accuracy for detecting AD in patients with DS. However, for the diagnosis of MCI, this battery showed poor to good diagnostic accuracy. CONCLUSION: The findings highlight important limitations of the current assessment available for the screening of mild cognitive impairment and AD in patients with DS and support the need for more clinical trials to ensure better screening for this highly at-risk population.
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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 | 0.025 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.042 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".