Effect of Donepezil on Cognition in Severe Alzheimer's Disease: A Pooled Data Analysis
Bibliographic record
Abstract
To better characterize response to donepezil in patients with severe AD, Severe Impairment Battery (SIB) data were pooled from four donepezil clinical trials (N=904). Changes in SIB total and domain scores from baseline to week 24 were compared between placebo and donepezil treatment groups (observed case analysis). Analyses were stratified by baseline severity (Mini-Mental State Examination [MMSE] scores 1-5, 6-9, 10-12 and 13-17) to allow investigation of responses at different stages of cognitive impairment. Relationships to global and functional measures were explored. The difference between donepezil- and placebo-treated patients in least squares (LS) mean change in SIB total scores from baseline to week 24 was 6.22 (p < 0.0001, Cohen's d, 0.53). Treatment-placebo differences were statistically significant for each baseline severity stratum, being greatest for the MMSE 6-9 stratum (LS mean difference, 7.60; p < 0.0001, Cohen's d, 0.66). Treatment-placebo differences in LS mean change in SIB domain scores significantly favored donepezil for seven of nine domains (range, p = 0.0056 to p < 0.0001; Cohen's d, 0.17-0.48). Change in total SIB score correlated significantly with change in measures of activities of daily living and global status. These results indicate that donepezil provides cognitive benefits in patients with severe AD, including those most markedly impaired. The treatment effect size and correlation between improvements in SIB scores and functional and global outcome measures suggest the drug-placebo differences are clinically meaningful.
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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".