P4‐314: Evaluating neuropsychiatric symptoms in Alzheimer's disease: A novel approach using bubble plots to evaluate donepezil treatment effects
Bibliographic record
Abstract
The relationship between the degree of measurable cognitive impairment and the pattern of neuropsychiatric symptoms (NPS) in AD has been generally characterized. Better understanding of this relationship could help predict NPS symptom targets as well as their potential responsiveness to treatment intervention. The objective of these analyses was to investigate a novel analytical technique relating NPS across individual strata of MMSE scores in patients with AD treated with placebo or donepezil. Data were pooled from 1106 patients with mild to severe AD (MMSE: 5–26) who were enrolled in 5 randomized, double-blind, placebo-controlled studies of donepezil and who had available data collected using the 10/12-item Neuropsychiatric Inventory (NPI). Bubble matrices were generated for the placebo and donepezil groups by plotting individual baseline MMSE scores (y-axis) against the 12 NPI items (x-axis). A bubble was fitted into each matrix cell with the size of bubble representing the magnitude of effect size (NPI raw mean change/SD at week 24 for corresponding NPI item and MMSE score). Treatment effects were then analyzed by evaluating differences between the placebo and donepezil matrices and measuring the number and magnitude of positive and negative changes. Statistical analyses were performed using the McNemar and Wilcoxon Signed-Rank tests. Comparing overall bubble patterns between placebo and donepezil (N = 241 pairs) showed a significant donepezil treatment effect (57% of bubble cells showed a positive change, P<0.024). Changes between the placebo and donepezil matrices were predominantly positive for 10 of the 12 NPI items. When the magnitude of positive and negative changes was analyzed, an overall positive treatment effect was observed (P<0.049). Nine of the 12 NPI items showed an overall positive change in item score. Using this bubble plot analytic technique, it is possible to evaluate an overall effect on individual NPS at each level of MMSE score, as well as evaluate each individual item in a novel way. This approach may assist in the challenge of identifying target symptoms and evaluating their responsiveness to treatment intervention.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".