Gabapentin adjunctive to risperidone or olanzapine in partially responsive schizophrenia: an open-label pilot study
Bibliographic record
Abstract
BACKGROUND: There is a great need in the treatment of schizophrenia for a drug, or drug combinations, to improve clinical response with fewer serious side effects. The objective of this study was to explore the therapeutic effects and tolerability of the anticonvulsant gabapentin as an adjunctive in the treatment of patients with partially responsive schizophrenia. METHODS: Ten consenting patients with a confirmed Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision diagnosis of schizophrenia were identified. All patients failed at least one 12-week treatment trial with risperidone or olanzapine. Gabapentin was added to ongoing antipsychotic treatment with olanzapine or risperidone for eight weeks. The primary outcome measure was the Positive and Negative Syndrome Scale (PANSS). Other scales included the Calgary Depression Scale (CDSS) and the Abnormal Involuntary Movement Scale (AIMS). Repeated-measures multivariate analysis of variance was utilized to examine changes in outcome measures over time with adjunctive treatment with gabapentin. RESULTS: There was a significant drop in the PANSS and CDSS scores at endpoint (week 8). There were no significant differences between the two treatment groups with regard to changes in all outcome measures or in AIMS score. The adjunctive treatments were well tolerated and side effects were transient. CONCLUSION: Gabapentin could be used successfully as an adjunct to novel antipsychotics in partially responsive schizophrenia. However, large controlled studies are needed to examine the effectiveness of gabapentin in psychotic disorders.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".