Meta-analysis of efficacy and safety of Donepezil in treating cognitive decline of Parkinson's disease
Bibliographic record
Abstract
Objective: To systematically evaluate the efficacy and safety of Donepezil in treating the \ncognitive decline in patients with Parkinson's disease (PD). Methods: Nine literature databases \nin domestic and abroad were searched by computer, and the search deadline was November \n2, 2020. The randomized controlled trials (RCT) using donepezil to treat the cognitive decline \nin patients with PD were screened. The MMSE (Mini-Mental State Exam) scores, MoCA \n(Montreal Cognitive Assessment) scores and adverse events of the included RCTs were \nextracted. Meta-analysis was implemented with RevMan 5.3 software, and the subgroup \nanalyses were conducted to evaluate the effects of mean age and mean course of disease on \nthe MMSE scores. Results: 14 RCTs with 1263 patients were selected, including 646 cases in \nthe experimental group and 617 cases in the control group. There were 1209 effective patients \nwho completed MMSE score, including 612 in the experimental group and 597 in the control \ngroup. Meta-analysis results showed that MMSE scores in the experimental group were \nhigher than that in the control group after using donepezil intervention, and the difference was \nstatistically significant [SMD=0.55, 95% CI (0.24, 0.87), P=0.0006]. It was also found that \nthe MoCA scores of experimental group were higher than that of control group after using \ndonepezil intervention, and the difference was statistically significant [SMD=1.25,95% CI \n(0.79, 1.72),P<0.00001]. Subgroup analysis showed that donepezil improved MMSE scores in \ndifferent subgroups, and the difference between each group was statistically significant. There \nwas no heterogeneity in the subgroup of mean age lower than 65 years, and the heterogeneity \nexisted between the subgroup of mean course of disease lower than 3 years and the subgroup \nof mean course of disease larger than or equal to 3 years. For adverse events, the incidence of \nadverse events in the experimental group was 22.18%. There was no heterogeneity between \nthe included studies (I2=0%), and the difference was not statistically significant [RR=1.18, 95% \nCI (0.95, 1.47), P=0.14]. Conclusion: The results of meta-analysis showed that donepezil can \nimprove the cognitive decline in patients with Parkinson's disease significantly, and it has good \nsafety.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.053 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".