Verbal repetition in patients with Alzheimer's disease who receive donepezil
Bibliographic record
Abstract
BACKGROUND: Current outcome measures for Alzheimer's disease (AD) drugs have been criticized as insufficiently patient-centred. One commonly unmeasured goal of patients and caregivers is verbal repetition. OBJECTIVES: We examined how often reducing repetition (of questions, statements or stories) was set as treatment goal, whether and when it responded, and how change in repetition correlated with change in other domains. METHODS: This is a secondary analysis of the open-label Atlantic Canada Alzheimer's Disease Investigation of Expectations study of donepezil for mild-moderate AD in 100 community-dwelling people. Goal Attainment Scaling, an individualized account of the goals of treatment, was the primary outcome measure. RESULTS: Reducing repetition was a treatment goal in 46%, who were not systematically different from others. Of 18 patients in whom repetition improved for 9 months, 83% (15) showed a response at 3 months. Early (3-month) response correlated best with the overall level of goal attainment (r = 0.74) and changes in leisure activities (r = 0.69) and social interactions (r = 0.68) compared with changes in cognition (r = 0.44) or behaviour (r = 0.11). Correlations with the ADAS-Cog and MMSE change scores remained only modest (at 12 months = -0.25 and 0.19, respectively). Correlations with the CIBIC-Plus were higher (-0.47 at 3 months and -0.43 at 12 months). CONCLUSION: Diminution of repetition is common, and appears to mark response to cholinesterase inhibition in some patients. Responders generally also show improved cognition and function, perhaps as an aspect of improved executive function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".