Semantic Memory Impairment for Biological and Man-Made Objects in Individuals With Amnestic Mild Cognitive Impairment or Late-Life Depression
Bibliographic record
Abstract
OBJECTIVE: Amnestic mild cognitive impairment (aMCI) and late-life depression (LLD) both increase the risk of developing Alzheimer disease (AD). Very little is known about the similarities and differences between these syndromes. The present study addresses this issue by examining the nature of semantic memory impairment (more precisely, object-based knowledge) in patients at risk of developing AD. METHODS: Participants were 17 elderly patients with aMCI, 18 patients with aMCI plus depressive symptoms (aMCI/D+), 15 patients with LLD, and 29 healthy controls. All participants were aged 55 years or older and were administered a semantic battery designed to assess semantic knowledge for 16 biological and 16 man-made items. RESULTS: Overall performance of aMCI/D+ participants was significantly worse than the 3 other groups, and performance for questions assessing knowledge for biological items was poorer than for questions relating to man-made items. CONCLUSION: This study is the first to show that aMCI/D+ is associated with object-based semantic memory impairment. These results support the view that semantic deficits in aMCI are associated with concomitant depressive symptoms. However, depressive symptoms alone do not account exclusively for semantic impairment, since patients with LLD showed no semantic memory deficit.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".