A large, naturalistic, community-based study of rivastigmine in mild-to-moderate AD:the EXTEND Study
Bibliographic record
Abstract
BACKGROUND: Previous studies in Alzheimer's Disease (AD) suggest a benefit from switching from one cholinesterase (ChE) inhibitor to another in the event of treatment failure on the index agent. This observational, open-label study sought to evaluate the efficacy of the ChE inhibitor rivastigmine on cognition, functional autonomy and behavior in patients with mild-to-moderate AD previously treated with other ChE inhibitors (switched patients) as well as in those previously ChE-inhibitor-naive (de novo users). METHODS: Patients were eligible for a switch if they experienced a lack or loss of efficacy or had experienced intolerance to prior ChE inhibitor therapy. Rivastigmine was initiated at a dose of 1.5 mg b.i.d. and titration was done as per standard medical practice. Efficacy was assessed using the mini-mental state examination (MMSE) and an abbreviated version of the Clinician's Global Impression of Change (CGI-C) at Month 3 and Month 6. Caregiver burden was also assessed at Month 6 using a self-rated scale. RESULTS: Overall, 2633 subjects were enrolled in this study. The mean MMSE improved from 20.6 at baseline to 21.5 at Month 6. More patients improved than deteriorated on every domain of the CGI-C. Caregivers felt less burdened after the 6 month evaluation period. Efficacy parameters demonstrated favorable results for both de novo and switched patients, but more so in the first group. LIMITATIONS: Open-label studies have an inherent potential for bias by both the caregiver and the physician. In this study, there was also a large percentage of missing patient records for each of the follow-up visits (Months 3 and 6). CONCLUSIONS: Patients with mild-to-moderate AD switched from previous ChE inhibitor therapy to rivastigmine can obtain measurable benefits, although the treatment effect may be less than in de novo patients. Further research into switching cholinesterase inhibitors is warranted.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".