Role of Cognitive Enhancer Therapy in Alzheimer’s Disease with Concomitant Cerebral White Matter Disease: Findings from a Long-Term Naturalistic Study
Bibliographic record
Abstract
BACKGROUND: Evidence is lacking for cognitive enhancer therapy in patients with Alzheimer's disease (AD) and concomitant cerebrovascular disease (mixed AD) as such patients would have been excluded from clinical trials. Earlier studies of mixed AD have focused on large vessel cerebrovascular disease. The influence of small vessel cerebrovascular disease (svCVD) in the form of white matter hyperintensity (WMH) on treatment outcomes in mixed AD has not been addressed. OBJECTIVE: In this long-term naturalistic study, we evaluated the effectiveness of cognitive enhancers in patients with mixed AD with svCVD. METHODS: We conducted a retrospective analysis of a prospective clinical database from a memory clinic of a tertiary hospital. Magnetic resonance imaging WMH was used as a marker of svCVD. Demographic, cognitive, and treatment data were analysed. Linear mixed models with patient-specific random effects were used to evaluate cognitive outcomes over time while adjusting for confounders. RESULTS: Patients with mixed AD (n = 137) or AD without svCVD (pure AD) (n = 28) were studied over a median duration of 28.7 months. Patients with mixed AD had a higher prevalence of hypertension (62.8 vs. 35.7 %, p = 0.011). The majority (75.2 %) of the study sample were managed with monotherapy. Mini Mental State Examination (MMSE) scores decreased over time (-0.04, p = 0.007), and the decrease was similar for both diagnosis groups (-0.03, p = 0.246). Annual estimated mean MMSE decline was 0.84 for pure AD and 0.48 for mixed AD. Similar trends were observed with Montreal Cognitive Assessment (MoCA) scores, with annual estimated mean reduction of 0.72 and 0.48 for pure AD and mixed AD, respectively. CONCLUSION: Cognitive enhancers are effective in slowing the rate of cognitive decline in patients with AD with svCVD. These findings would need to be confirmed in randomized clinical trials.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.000 |
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".