Size of the treatment effect on cognition of cholinesterase inhibition in Alzheimer's disease
Bibliographic record
Abstract
BACKGROUND: Six cholinesterase inhibitors (ChEIs) have been tested in people with Alzheimer's disease, using methods currently required for regulatory approval. The clinical importance of their treatment effects is controversial. OBJECTIVE: To determine whether cholinesterase inhibition produces treatment effects in Alzheimer's disease that are large enough to be clinically detectable. METHODS: Overview analysis of published trials of ChEIs in which the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) and a global clinical measure were primary outcomes. Two quantitative summary measures of the treatment effect (Cohen's d and the standardised response mean (SRM)) were calculated and presented as funnel plots. Observed cases analyses and intention to treat (ITT) with the last observation carried forward (LOCF) analyses were compared. RESULTS: The median Cohen's d effect sizes (ES) using ITT samples with LOCF for the ADAS-Cog were: low dose of a ChEI (n = 8 studies) median ES = 0.15, range = 0.03-0.22; medium dose (n = 13) median ES = 0.23, range = 0.12-0.29; high dose (n = 9) median ES = 0.28, range = 0.01-0.31. In general, the ES were larger when calculated as SRMs (for example, high dose ChEI studies, median SRM = 0.47; range = 0.30-0.63) and highest in the observed cases analyses (for example, high dose median SRM = 0.56, range = 0.35-0.78). Global clinical scales produced similar estimates of ES (for example, high dose ChEI, ITT/LOCF median Cohen's d = 0.29, range = 0.20-0.47). CONCLUSIONS: ChEIs produce small-moderate effect sizes in clinical trials which are reproducible and demonstrate a dose response. Better descriptions of the patterns of treatment response are needed to guide individual patient decisions about the effectiveness of treatment, but group effects are evident and appear large enough to be clinically detectable.
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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 | 0.208 | 0.259 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.023 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".