Effect of donepezil hydrochloride on changes of cognitive function and Montreal cognitive assessment scores in patients with mild cognitive impairment
Bibliographic record
Abstract
Objective To investigate the effect of donepezil hydrochloride on changes of cognitive functions and Montreal cognitive assessment (MoCA) scores in patients with mild cognitive impairment (MCI). Methods A total of 50 patients diagnosed as having MCI,admitted to our hospital from September 2010 to March 2011, were recruited; these patients were randomly assigned into treatment group (treated with donepezil hydrochloride,n=30) and control group (treated with Ginkgo biloba,n=20); the changes of MoCA scores were noted at 12 and 24 weeks after treatment.The data were analyzed by the statistical software SPSS 10.0. Results MoCA scores of treatment group 12 and 24 weeks after treatment were significantly higher than those before treatment (P＜0.05); MoCA scores of treatment group were significantly higher than those of control group 12 and 24 weeks after treatment (P＜0.05).Mann-Whitney rank sum test showed significant improvement in digit span,verbal fluency and delayed memory after the treatment of donepezil hydrochloride for 12 weeks as compared with those before treatment (P＜0.05). Conclusion By MoCA scale,MCI patients could get early diagnosis; MCI patients with early diagnosis and early application of donepezil hydrochloride will delay their entry into the course of AD. Key words: Mini Mental State Examination; Montreal Cognitive Assessment; Mild cognitive impairment; Donepezil hydrochloride
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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) | 0.001 | 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".