Patterns of decline and evidence of subgroups in patients with Alzheimer's disease taking galantamine for up to 48 months
Bibliographic record
Abstract
BACKGROUND: Lacking long-term placebo-controlled trials of cholinesterase inhibitors (ChEIs) in Alzheimer's disease, how decline is modeled is crucial to interpreting the effects of long-term, open-label use. We aimed to: 1) understand changes in cognition and function in people taking galantamine; (2) identify treatment subgroups. METHODS: This is an open-label long-term extension (up to 48 months) of two placebo-controlled clinical trials from Europe and Canada. At baseline, 240 patients with mild-moderate Alzheimer's disease at baseline had received galantamine continuously for 36 months. The trajectory of scores on the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) and the Disability Assessment for Dementia (DAD) was modeled over 48 months. Goodness of fit was compared using normalized residuals and by calculating correlation coefficients between observed and fitted data. Iterative K-means clustering identified subgroups. RESULTS: Of 240 patients, 51 (21%) withdrew between months 36-48 (nine died and 12 with adverse events). The mean ADAS-Cog worsened from 22.6 (mean)+/-8.6 (SD) at baseline to 31.3+/-3.1, with similar changes in the DAD. The logistic function fit better (ADAS-Cog norm of residuals=2.05, r=0.993; DAD=2.21, r=0.998) than the commonly used Stern equation (ADAS-Cog=4.22, r=0.944; DAD=7.51, r=0.986). K-means clustering identified three stable groups; least decline (n=82), persistent progression (n=75), and an intermediate group (n=82). Initial conditions were important: the mean ADAS-Cog score for those with least decline was 15+/-8, better than both the intermediate group (20+/-9) and persistent progressors (30+/-10; p<0.001). CONCLUSIONS: Alzheimer's disease patients show variable long-term decline that is best modeled as a logistic function, not as linear decline. Over 48 months, about one-third of patients showed little decline, while one-third showed important decline.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".