Augmentation of atypical antipsychotics with valproic acid. An open‐label study for most difficult patients with schizophrenia
Bibliographic record
Abstract
OBJECTIVE: Most difficult inpatients with schizophrenia are in serious needs but obviously underrepresented in clinical trials. METHODS: Very challenging patients received open-label treatment with atypical antipsychotics concurrently augmented with valproic acid. The primary outcome was the newly developed Functional Assessment for Comprehensive Treatment of Schizophrenia (FACT-Sz). Patients improving more than 20 points were classified as responders. RESULTS: Mean age and illness duration of 28 participants (22 male) were 42 y.o. and 20 years, respectively. They had spent a half of their life admitted after the onset. The average Brief Psychiatric Rating Scale (BPRS) and Clinical Global Impression-Severity (CGI-S) were very severe at 79 and 6.1, respectively, with the baseline Global Assessment of Functioning (GAF) of as low as 21. As a result of augmentation, there were nine responders, 12 partial responders, and seven non-responders including only two patients who got worse. The main antipsychotics were mostly either risperidone or olanzapine. Mean maximum oral dose and blood level of valproic acid were 1907 mg and 91.7 microg/ml, respectively. Overall significant improvements whilst to an inadequate degree were noted in clinical parameters. Valproate augmentation was generally well tolerated but serious adverse effects included thrombocytopenia, anaemia and sedation/falls. CONCLUSIONS: While these preliminary results need to be tested against tenacious monotherapy or polypharmacy involving clozapine, augmenting atypical antipsychotics with valproic acid can be useful for very severe schizophrenia.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".