Effect of Donepezil on Cognitive Disorders Due to the Selective Serotonin Reuptake Inhibitors in the Patients with Major Depressive Disorder
Bibliographic record
Abstract
Introduction: Many factors cause cognitive impairment, including medication, such as selective serotonine reuptake inhibitor drugs use. On the other hand, many drugs are used in cognitive impairment therapy, including donepezil, which act by inhibiting the cholinesterase enzyme and increase brain acetylcholine. Methods: This study was a double-blind controlled randomized controlled clinical trial on the 73 numbers of 20-50 years-old patients treated with selective serotonine reuptake inhibitor drugs using Montreal Cognitive Assessment Test. They were randomly divided into two groups: placebo recipient and donepezil recipient. The Montreal Cognitive Test- reliability 92% and IC 83% - was performed two months after drug administration in both groups.The results were analyzed by Mann-Whitney, Chi-Square, T-test and has been reviewed by SPSS Inc., Chicago, IL; Version 16. Results: There was a significant difference in the Montreal Cognitive Test score before and after the intervention in the Donepezil group as the score increased. (Paired-T Test & P-Value < 0.0001). There was also a significant difference in Montreal Cognitive Test scores before and after the intervention in the placebo group (Paired-T Test & P-Value < 0.0001) as the score dropped.Conclusion: Cholinesterase inhibitors, such as donepezil, have had beneficial effects in improving cognitive impairment caused by selective serotonin reuptake inhibitor drugs compared to placebo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".