Cognitive testing for early detection of Alzheimer's disease in people with Down syndrome: A systematic review and meta‐analysis
Bibliographic record
Abstract
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). The development of brain lesions appears years before AD. Through this progressive neuronal loss, patients can firstly begin to have a perception of cognitive difficulties, i.e., a subjective cognitive decline (SCD). This preclinical phase ends with the onset of mild cognitive impairment (MCI) which is characterized by a set of cognitive symptoms. Knowing that people with DS have already pre‐existing cognitive deficits, screening at the preclinical and clinical stages of AD therefore cannot be done through traditional cognitive tests. The aim of this systematic review is to synthesis cognitive assessment for the diagnosis of AD, MCI or SCD in people with DS. Method A meta‐analysis is in development. Articles were collected from Pubmed and Psychinfo databases through keywords related to three categories: 1) DS, 2) AD and 3) assessment. The inclusion criteria are studies 1) with participants over 18 years old with DS, 2) which assess cognitive abilities in the context of screening for AD or its early stages, 3) presenting the effect sizes or data allowing their calculation. Result Eighteen articles met the inclusion criteria. Fifty cognitive tests for the diagnosis of AD, MCI or SCD were found. From these tests, fourteen evaluated cognition through an informant questionnaire and thirty‐six directly evaluated the patient’s cognitive abilities. Conclusion Theoretical and practical implications of these results are discussed.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".