Clustering and Switching Strategies During Verbal Fluency Performance Differentiate Alzheimer's Disease and Healthy Aging
Bibliographic record
Abstract
Clustering and switching strategies during phonemic and semantic verbal fluency tasks as defined by Troyer et al. (1997), Abwender et al. (2001), and Lanting et al. (2009) were compared using archival data to determine which scoring procedures best differentiate healthy older adults (n = 26) from individuals with early-stage Alzheimer's disease (AD, n = 26). Total word production showed the largest group difference, especially for semantic fluency. The AD group produced fewer switches when compared to the healthy control group, whereas the groups did not differ in cluster size. The AD group also accessed fewer novel semantic subcategories, presumably due to reduced access to semantic memory storage rather than lower processing speed. Clustering and switching scores on the phonemic task did not add information above total words produced, consistent with previous research indicating these variables are most informative in relation to semantic fluency.
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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 | 0.003 |
| 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 | 0.001 | 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".