Verbal fluency patterns in amnestic mild cognitive impairment are characteristic of Alzheimer's type dementia
Bibliographic record
Abstract
Amnestic mild cognitive impairment (aMCI) represents a high-risk factor for Alzheimer's disease (AD) and is characterized by a selective decline in episodic memory. Although by definition aMCI is not associated with impaired verbal fluency performance, we examined relative differences between fluency tasks because AD is characterized by poorer semantic than phonemic fluency. Phonemic and semantic fluency trials were administered to 46 healthy controls, 33 patients with aMCI, and 33 patients with AD. Results revealed a progressive advantage (controls > aMCI > AD) in semantic, relative to phonemic fluency. Difference scores between tasks distinguished each group from the others with medium to large effect sizes (d) ranging from 0.49 to 1.07. Semantic fluency relies more on semantic associations between category exemplars than does phonemic fluency. This aMCI fluency pattern reflects degradation of semantic networks demonstrating that initial neuropathology may extend beyond known early changes in hippocampal regions. (JINS, 2006, 12, 570–574.)The data were collected in accordance with the guidelines of the Helsinki Declaration and approved by the Research Ethics and Scientific Review Committee of Baycrest Centre. This is an original submission. Portions of these data were presented at the annual meeting of the International Neuropsychological Society, February 2005, St. Louis, Missouri. There is no conflict of interest.
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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 | 0.005 |
| 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 | 0.000 |
| 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".