Staving off dementia through prevention of word retrieval problems in aging
Bibliographic record
Abstract
Abstract Background After the age of 40, everyone experiences instances of a temporary inability to retrieve a familiar word known as the annoying, and often embarrassing, tip‐of‐the‐tongue (TOT) phenomenon. The frequency of occurrence of TOTs is increased in individuals experiencing language decline associated with Alzheimer's disease. Current research demonstrates that building cognitive reserve may stave off the onset of dementia. Engaging in meaningful activities focused on word retrieval may afford the building of lexical reserve and, consequently, stave off the onset of language decline. Method 30 cognitively healthy seniors (65+) engaged in a daily practice with word finding activities on a website built to improve word retrieval. They practiced for one hour on 20 consecutive days. Before and after the daily practice, they completed a category fluency task in which they generated responses to 11 semantic categories. The goal was to evaluate potential benefits of practice on reducing the frequency of TOTs and improving word retrieval abilities. Result A paired‐samples t‐test performed on pre‐ and post‐intervention data showed a significant improvement in the total score across all categories, suggesting benefits from daily practice. Conclusion The results suggest that in healthy seniors, engaging in activities focused on word retrieval may improve word finding and reduce TOTs. Ultimately, this could boost cognitive reserve necessary to stave off the onset of language decline in dementia. Practice effects on TOTs in spontaneous speech are currently being analyzed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".