L-Arginine Add-On Treatment for Schizophrenia: A Randomized, Double-Blind, Placebo-Controlled, Crossover Study
Bibliographic record
Abstract
OBJECTIVE: Current drug treatments for schizophrenia are only partially effective and combination/augmentation strategies are commonly used. Nitric Oxide (NO) may play a role in the pathophysiology of schizophrenia. L-arginine is the precursor of NO. In this study, we aimed to investigate whether L-arginine add-on to current medication might improve positive, negative, and depressive symptoms in schizophrenia/ schizoaffective disorder patients in partial remission. METHOD: The study was designed as a randomized, double-blind, placebo-controlled, crossover study of L-arginine 3 g b.i.d. as an add-on treatment to the patients' usual medication. Twelve patients diagnosed with schizophrenia/schizoaffective disorder were included. The duration of the treatment was 3 weeks, with a wash-out period of 7 days before alternation for the second arm. Psychopathology was assessed with the Positive and Negative Syndrome Scale (PANSS), the Calgary Depression Scale for Schizophrenia (CDSS), and the Clinical Global Impression (CGI) scales. The study was supported by Hacettepe University Scientific Research and Development Office (Project No: 011D0110101013) (Clinical Trials.gov Identifier: NCT02398279). RESULTS: Our analyses revealed that L-arginine 6 g/day add-on to usual treatment was not superior to placebo for positive, negative, and depressive symptoms associated with schizophrenia as assessed with PANSS, CDSS and CGI scales. CONCLUSION: In our study, L-arginine did not seem to have an effect on schizophrenia symptoms. Studies with a larger sample size, with higher doses of L-arginine, and with a longer duration are needed for a definite conclusion.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".