Treatment of a Whole Population Sample of Alzheimer’s Disease with Donepezil over a 4-Year Period: Lessons Learned
Bibliographic record
Abstract
BACKGROUND: In the UK it is recommended that acetylcholinesterase inhibitors be restricted to patients with moderate Alzheimer's disease, and progress monitored within specialist clinics. OBJECTIVE: To describe a cohort of patients with Alzheimer's disease from a whole city population treated with donepezil, and to analyse outcomes over 4 years. METHODS: Historical cohort design: 88 patients recruited 1997-1998, assessed at baseline with 4-year follow-up, using an agreed protocol and validated measures: survival, retention in treatment, cognition, non-cognitive symptoms, weight change, carer stress. RESULTS: 64.7% remained on treatment beyond 6 months, 57.9% beyond 1 year and 12.5% beyond 4 years. 56% remained alive at 4 years - almost twice the number predicted. Mean MMSE score amongst patients in treatment did not deteriorate over 4 years. Survival, retention in treatment, maintenance/improvement of cognition was greater with high baseline MMSE. Non-cognitive symptoms, carer stress and weight change remained low throughout. CONCLUSIONS: A minority of people with dementia from the population (88 of potential 2,000 at outset, 11 by 4 years) received treatment. Benefits for individuals were confirmed, especially for those with mild impairment. Expenditure on medication was modest in a population context. These findings question recent guidance from the National Institute for Clinical Excellence, which would restrict therapy to patients with moderate cognitive impairment.
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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 | 0.005 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".