Clinical efficacy of donepezil hydrochloride in treatment of elderly patients with mild cognitive impairment and their EEG feature evaluation
Bibliographic record
Abstract
Objective To explore the clinical efficacy of donepezil hydrochloride in the treatment of elderly patients with mild cognitive impairment (MCI) and changes of their electroencephalogram (EEG) and event related potential (ERP) P300. Methods One hundred and fifteen patients with MCI,admitted to our hospital from January 2009 to March 2010,were randomly divided into treatment group (hydrochloride donepezil combined with ginkgo biloba,n=58) and control group (single-use ginkgo biloba,n=57).Mini-mental state examination (MMSE) scores,changes of P300 and EEG were used to assess the effect of donepezil hydrochloride. Results After 12 months of treatment,MMSE scores in both groups improved,and the MMSE scores in the treatment group were significantly higher than those in the control group or treatment group before treatment (P＜0.05). The abnormal rate of EEG in the treatment group (25.86％) was significantly lower than that in the control group (45.61％),and the difference was statistically significance (P＜0.05).The latency of P300 was decreased and the amplitude was increased in both groups,but more obvious changes were observed in the treatment group as compared with those in the control group and treatment group before treatment (P＜0.05).The clinical efficacy of treatment group was significantly better than that of control group (P＜0.05). Conclusion As compared with ginkgo biloba,donepezil can significant improve the treatment efficacy of cognitive ability in patients with MCI and the state of brain electrical activity,which indicates that donepezil is valuable in the treatment of elderly MCI. Key words: Mild cognitive impairment; Donepezil hydrochloride; Ginkgo bilob; Mini-mental state examination; Electroencephalogram; Event-related potential; The elderly
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".