Donepezil for dementia with Lewy bodies: meta‐analysis of multicentre, randomised, double‐blind, placebo‐controlled phase <scp>II</scp>, <scp>III</scp>, and, <scp>IV</scp> studies
Bibliographic record
Abstract
Abstract Background Current evidence for the management of symptoms associated with dementia with Lewy bodies (DLB) using donepezil is limited. We conducted a meta‐analysis of three randomised controlled trials of donepezil in patients with DLB to investigate the overall efficacy of donepezil on Mini‐Mental State Examination (MMSE), Neuropsychiatric Inventory (NPI), and Clinician's Interview‐Based Impression of Change‐plus Caregiver Input (CIBIC‐plus). Methods A meta‐analysis was performed using the data of 312 patients administered placebo or 10 mg donepezil. Overall mean score differences for MMSE, NPI‐2, and NPI‐10 from baseline to week 12 and their 95% confidence intervals (CI) were estimated. For CIBIC‐plus, which was transformed from a seven‐point grade to a dichotomous outcome (improvements/no improvements), odds ratio (OR) and its 95% CI were estimated. Random‐effects models were used, and heterogeneity was evaluated using the Cochrane's Q test and I 2 statistic. Results Heterogeneity was suspected for NPI‐2 ( P < 0.05; I 2 = 87.2%) and NPI‐10 ( P < 0.05; I 2 = 67.7%) while it was not suspected for MMSE ( P = 0.23; I 2 = 32.4%) and CIBIC‐plus ( P = 0.26; I 2 = 19.8%). The overall mean MMSE score difference (mean difference: 1.50; 95% CI, 0.67–2.34) and the overall odds of improving CIBIC‐plus (OR: 2.20; 95% CI, 1.13–4.26) from baseline to week 12 were higher in the donepezil group than in the placebo group. Conclusion Results of our meta‐analysis indicated overall efficacy of donepezil on cognitive impairment and global clinical status in patients with DLB.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".