The Effect of Melatonin Adjuvant on Cognitive Function and Melatonin Levels in Schizophrenia Patients
Bibliographic record
Abstract
Background: Schizophrenia is an important and serious mental disorder. It has been studied that serum melatonin levels were significantly decreased in schizophrenia patients. Objective: To investigate the effect of adjuvant melatonin on cognitive function and serum melatonin levels in schizophrenia patients receiving risperidone therapy Methods: This study used a randomized controlled double-blind design. Forty-four male schizophrenic patients were successfully enrolled and randomized into two groups: 22 treatment patients who received risperidone therapy (4-6 mg/day) and melatonin (5 mg/day) and 22 control patients who received risperidone and placebo. The Montreal Cognitive Assessment Indonesian version (MoCA-INA) scale and serum melatonin levels were measured before and after 8 weeks of therapy. Data were analyzed using chi-squared, independent samples t-test or Mann-Whitney and Spearman correlation tests. Results: Changes in MoCA-Ina scores were significantly different between the treatment and control groups (p<0.001), particularly in the visuospatial/executive, language, memory and orientation dimensions (p<0.05). The change in melatonin levels was also significantly different between groups (p<0.05). The decrease in melatonin levels in the treatment group was about three times lower than in the control group (-4.34 pg/mL vs. -13.61 pg/mL). There was no significant correlation between melatonin levels and MoCA-Ina scores. Conclusion: Adjuvant melatonin could improve cognitive function and slow the rate of melatonin decline in schizophrenia patients receiving risperidone.
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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".