Clozapine–treatment–resistant schizophrenia successfully managed with brexpiprazole combination therapy and online meta-cognitive training: A case report
Bibliographic record
Abstract
Clozapine is considered one of the best effective strategies in treatment-resistant schizophrenia (TRS), despite up to 60% of clozapine-treated patients not adequately and/or fully achieving a clinical response either partial remission (clozapine-resistant treatment, CRT). Hence, several combination strategies to clozapine have been investigated in CRT. Hereby, we describe a 50-year-old CRT male patient successfully managed with brexpiprazole augmentation strategy. The patient was assessed at the baseline, weekly up to week 4 and monthly after that up to week 16, with the following psychopathological rating scales: Clinical Global Impression - Improvement (CGI-I), Brief Psychiatric Rating Scale (BPRS), Positive and Negative Syndrome Scale (PANSS) Calgary Depression Scale for Schizophrenia (CDSS), Montgomery-Asberg Depression Rating Scale (MADRS), Young Mania Rating Scale (YMRS), Hamilton Rating Scale for Anxiety (HRS-A), Barnes Akathisia Scale (BARS), and Abnormal Involuntary Movement Scale (AIMS). At 1-month follow-up, he already showed a considerable improvement at the psychopathological assessment (63% total PANSS reduction). At 4-month follow-up, a further 50% PANSS total reduction was observed from the baseline. After 1-year of clozapine-brexpiprazole treatment, the patient was also administered an online metacognitive training (MCT) as adjunctive intervention, by reporting a further clinical improvement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".