Adjunctive agents to antipsychotics in schizophrenia: a systematic umbrella review and recommendations for amino acids, hormonal therapies and anti-inflammatory drugs
Bibliographic record
Abstract
QUESTION: This umbrella review and guidelines aimed to provide evidence to support the rational choice of selected adjunctive therapies for schizophrenia. STUDY SELECTION AND ANALYSIS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and World Federation of Societies of Biological Psychiatry (WFSBP)-grading recommendations, 63 randomised control trials (RCTs) (of which 4219 unique participants have completed the RCTs) and 29 meta-analyses were analysed. FINDINGS: Provisional recommendations (WFSBP-grade 1) could be made for two molecules in augmentation to antipsychotics: (1) N-acetyl-cysteine (NAC, 1200-3600 mg/day, for >12 consecutive weeks) in improving negative symptoms, general psychopathology (positive and negative syndrome scale for schizophrenia (PANSS) general psychopathology factor (G)-G subscale), with the RCTs with the longer duration showing the most robust findings; (2) polyunsaturated fatty acids (3000 mg/day of eicosapentaenoic acid, for >12 weeks) in improving general psychopathology. Weaker recommendations (ie, WFSBP-grade 2) could be drawn for sarcosine (2 g/day) and minocycline (200-300 mg/day) for improving negative symptoms in chronic schizophrenia (not early schizophrenia), and NAC for improving positive symptoms and cognition. Weak recommendations are not ready for clinical practice. There is provisional evidence that oestrogens and raloxifene are effective in some patients, but further research is needed to determine their benefit/risk ratio. CONCLUSIONS: The results of this umbrella review should be interpreted with caution as the number of RCTs included in the meta-analyses was generally small and the effect sizes were weak or medium. For NAC, two RCTs with low risk of bias have provided conflicting results and the WFSBP-grade recommendation included also the results of meta-analyses. These drugs could be provisionally prescribed for patients for whom no other treatments have been effective, but they should be discontinued if they prove ineffective.
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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 | 0.087 | 0.169 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".