Cognitive Change in Donepezil Treated Patients with Vascular or Mixed Dementia
Bibliographic record
Abstract
BACKGROUND: vascular dementia (VaD) and mixed Alzheimer's disease (AD/VaD) are common. How best to monitor treatment is not clear. Our objective was to compare responsiveness and construct validity of change scores, following donepezil treatment, of the standardized Mini-Mental State Examination (sMMSE) and other measures potentially usable in primary care. METHODS: A six-month, outcome measurement study. The Disability Assessment for Dementia (DAD), CLOX-1 and 2, Phonetic Fluency, a short Neuropsychiatric Inventory, (the NPI-Q), Clinical Global Impression (CGI) and the SymptomGuide™ (SG) were measured. Construct validity was tested by correlating change scores, and responsiveness by calculating standardized response means (SRMs). RESULTS: Of 148 treated patients, 116 completed. The mean sMMSE increased by 0.7 (95% Confidence Interval (CI) = -0.005, 1.41; p=0.06; SRM= 0.15). There was no statistically significant difference in the DAD. The NPI-Q (-1.4; 95% CI = -2.08, -0.72; p<0.01; SRM=0.24), CLOX-1 (0.9; 95% CI = 0.19, 1.61; p<0.01; SRM=0.21), CLOX-2 (0.9; 95% CI = 0.17, 1.63; p=0.03; SRM=0.26), Phonetic Fluency (0.9; 95% CI = 0.19, 1.61; p=0.02; SRM=0.21) and SG (0.35; 95% CI = 0.20,0.51; p<0.01; SRM=0.28) each detected significant improvement. The CGI suggested improvement in 74 completers (64%) - mostly "minimal" (44/116, 38%) - while 21/116 (18%) were worse. Change scores at 24 weeks were at best modestly correlated with each other (range -0.22 to 0.30). DISCUSSION: Different measures showed different responsiveness, in a setting in which the mean treatment effect seems to have been small, but clinically detectable. Patient-centered and executive function measures might be useful in vascular and mixed dementia.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".