The The Indonesian Version of Montreal Cognitive Assessment (MoCA-Ina): The Difference Scores Between Male Schizophrenia Prescribed by Risperidone and Adjunctive of Donepezil in Public Hospital of Dr Pirngadi Medan, Indonesia
Bibliographic record
Abstract
BACKGROUND: Prescribing donepezil as an addition in reducing the cognitive dysfunctions among schizophrenia patients that have been given by antipsychotic (risperidone and olanzapine) is commonly used. Also, to determine the presence of the dysfunctions, an assessment is conducted by Montreal Cognitive Assessments based on Indonesian version (MoCA-Ina) to provide a more understandable test. AIM: To determine the score differences of MoCA-Ina between male patients of schizophrenia prescribed with only risperidone, and those with the addition of Donepezil within a certain interval of times. SETTINGS AND DESIGN: It is a pre-post-test experimental design with non-probability of consecutive sampling. METHODS: The study involved 48 of schizophrenia patients who have been prescribed fixed dose risperidone for 4 mg/day orally, and 24 people who were the intervention group were prescribed with the additional of 5 mg of donepezil per day started from the first until sixth week, followed by the increased dosages to 10 mg until twelfth week. These patients were recruited from the Public Hospital of Dr Pirngadi Medan, Indonesia, under the Department of Psychiatry. Then, the statistical data were analysed by Mann Whitney U, Friedman, and Wilcoxon, followed by analysing of SPSS version 21. RESULTS: The addition of five mg of Donepezil increased the MoCA-Ina score significantly compared to those who only prescribed with risperidone during all weeks of observation. CONCLUSION: Based on the results, the addition of donepezil increased the score level of the MoCA-Ina in the intervention group.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".