An Examination of Semantic Impairment in Amnestic MCI and AD: What Can We Learn From Verbal Fluency?
Bibliographic record
Abstract
INTRODUCTION: The Verbal Fluency Test (VF) is commonly used in neuropsychology. Some studies have demonstrated a marked impairment of semantic VF compared to phonemic VF in Alzheimer's disease (AD). Since amnestic Mild Cognitive Impairment (aMCI) is associated with increased risk of conversion to incident AD, it is relevant to examine whether a similar impairment is observed in this population. The objective of the present empirical study is to compare VF performance of aMCI patients to those of AD and elderly controls matched one-to-one for age and education. METHOD: Ninety-six participants divided into three equal groups (N = 32: AD, aMCI and Controls) were included in this study. Participants in each group were, on average, 76 years of age and had 13 years of education. A repeated measures ANOVA with the Group (AD, aMCI, NC) as between-subject factor and the Fluency condition ("P" and "animals") as within-subject factor was performed. T-tests and simple ANOVAs were also conducted to examine the interaction. RESULTS: There was a significant interaction between the groups and the verbal fluency condition. In AD, significantly fewer words were produced in both conditions. In contrast, participants with aMCI demonstrated a pattern similar to controls in the phonemic condition, but generated significantly fewer words in the semantic condition. CONCLUSION: These results indicate a semantic memory impairment in aMCI revealed by a simple, commonly-used neuropsychological test. Future studies are needed to investigate if semantic fluency deficits can help predict future conversion to AD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".