Deficits in Different Cognitive Domains Predict the Progression of Amnestic Mild Cognitive Impairment: A Literature Review
Bibliographic record
Abstract
Introduction: Mild cognitive impairment (MCI) is a heterogeneous syndrome in which older adults show cognitive deficits that do not interfere with daily living. Amnestic mild cognitive impairment (aMCI) is a subtype of MCI where episodic memory is significantly impaired and considered the transition stage between normal aging and Alzheimer’s disease (AD). The cognitive profiles of individuals with aMCI may predict various trajectories and inform the risk of AD conversion. Given that cognitive domains beyond memory including language and executive functioning (EF) may contribute to the progression of aMCI, this paper will examine how deficits in these three domains can be used to predict trajectories of the syndrome. Methods: PUBMED, EMBASE, and CINAHL databases were used to screen for studies to construct this systematic review. A total of 20 studies were reviewed. Results: Functional changes in memory were observed including the diminished performance in the encoding and recognition phases of episodic memory, associative recall, rapid forgetting, and pattern separation in individuals with aMCI relative to age-matched peers. Overall EF and all three core EF components studied were impaired to similar extents. Dynamic working memory and verbal memory performance was impaired to a greater extent in individuals with multiple-domain aMCI (a more severe subtype of aMCI) relative to single-domain aMCI. Language impairments were associated with AD pathology, including verbal fluency deficits and semantic verbal fluency patterns, all of which were further impaired in individuals with multiple-domain aMCI relative to single-domain aMCI. Discussion: Structural and functional changes in the medial temporal lobe (MTL) underlie various deficits in memory, EF, and language domains. Research suggests that individuals with single-domain aMCI perform more similarly with healthy controls, while those with multiple-domain aMCI perform more similarly with individuals with AD. This is likely attributed to cognitive domains beyond memory having a drastic impact on aMCI pathogenesis. Conclusion: Preliminary evidence offers support for a more nuanced use of cognitive profiles to determine future outcomes and take appropriate clinical action earlier for improved prognosis and therapeutic plan development. Earlier formulation of treatment methods could drastically increase the likelihood of slowing or even reversing aMCI pathogenesis.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".