Efficacy of cholinesterase inhibitors treatment in dementia with Lewy bodies: A 3-year follow-up ‘real world’ study
Bibliographic record
Abstract
BackgroundDementia with Lewy bodies (DLB) is the second most common dementia after Alzheimer's disease. Currently, no specific therapeutic agents are available for DLB. However, evidence of cholinergic deficits suggests that enhancing central cholinergic function may be a viable therapeutic approach.ObjectiveTo assess cognitive changes in DLB patients treated with cholinesterase inhibitors (ChEIs) in a real-world setting.MethodsThis retrospective study in a prospective database analyzed data from three dementia clinics between May 2012 and December 2022. Patients with DLB were divided into two groups: those treated with ChEIs and those untreated. Differences in changes in multiple cognitive-related scales between the two groups were analyzed.ResultsThe study included 204 DLB patients, with 133 (65.2%) in the ChEIs group and 71 (34.8%) in the non-ChEIs group. Initial demographic and clinical characteristics were similar between groups. Over time, patients in the ChEIs group showed significantly higher scores on the Mini-Mental State Examination and the Montreal Cognitive Assessment compared to the non-ChEIs group, indicating improved cognitive function. No significant differences were observed in activities of daily living scores.ConclusionsChEIs improved cognitive symptoms in DLB patients in the "real world" study. These findings are consistent with those from a previous small-sample randomized controlled trial. Longitudinal data indicate sustained benefits with continuous ChEIs use in three years. Overall, ChEIs show substantial potential for improving cognitive symptoms in DLB patients.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".