Adjunctive huperzine A for cognitive deficits in schizophrenia: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the efficacy of huperzine A (HupA), an isolate of Huperzine serrata, in the treatment of cognitive deficits in schizophrenia spectrum disorders. METHODS: PubMed, PsycINFO, Embase, Cochrane Library, Cochrane Controlled Trials Register, WanFang, Chinese Biomedical, and China Journal Net databases were searched from inception to 15 July 2015 for randomized controlled trials (RCTs) in English or Chinese of HupA augmentation of antipsychotic drug therapy versus placebo or ongoing antipsychotic treatment. RESULTS: Twelve RCTs (n = 1117) lasting 11.7 ± 6.0 weeks met inclusion criteria. All had been conducted in China. HupA outperformed comparators on the following outcome measures: the Wechsler Memory Scale-Revised including memory quotient (weighted mean difference (WMD: 10.59; 95% confidence interval (CI): 5.65, 15.53; p < 0.0001); Wechsler Adult Intelligence Scale-Revised including verbal intelligence quotient (IQ), performance IQ, and full IQ (WMD: 3.97 to 5.66; 95%CI: 0.20, 8.58; p = 0.01 to 0.00001); Wisconsin Card Sorting Test including response administer and non-perseverative errors (WMD: -12.79 to -12.29; 95%CI: -23.70, -0.88; p = 0.03 to 0.003). In studies using the Positive and Negative Syndrome Scale (n = 7)/Brief Psychiatric Rating Scale (n = 1), the differences in total score were significant (standard mean difference: -0.77; 95%CI: -1.27, -0.27; p = 0.002). All-cause discontinuation (risk ratio: 0.67; 95%CI: 0.36, 1.24; p = 0.20) and adverse events were similar between groups. CONCLUSIONS: This review suggests that adjunctive HupA is an effective choice for improving cognitive function for patients with schizophrenia spectrum disorders. More well-designed RCTs are needed to further confirm HupA's efficacy. Copyright © 2016 John Wiley & Sons, Ltd.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".