Clozapine augmentation strategies – a systematic meta-review of available evidence. Treatment options for clozapine resistance
Bibliographic record
Abstract
BACKGROUND: Treatment options for clozapine resistance are diverse whereas, in contrast, the evidence for augmentation or combination strategies is sparse. AIMS: We aimed to extract levels of evidence from available data and extrapolate recommendations for clinical practice. METHODS: We conducted a systematic literature search in the PubMed/MEDLINE database and in the Cochrane database. Included meta-analyses were assessed using Scottish Intercollegiate Guidelines Network criteria, with symptom improvement as the endpoint, in order to develop a recommendation grade for each clinical strategy identified. RESULTS: Our search identified 21 meta-analyses of clozapine combination or augmentation strategies. No strategies met Grade A criteria. Strategies meeting Grade B included combinations with first- or second-generation antipsychotics, augmentation with electroconvulsive therapy for persistent positive symptoms, and combination with certain antidepressants (fluoxetine, duloxetine, citalopram) for persistent negative symptoms. Augmentation strategies with mood-stabilisers, anticonvulsants, glutamatergics, repetitive transcranial magnetic stimulation, transcranial direct current stimulation or cognitive behavioural therapy met Grades C-D criteria only. CONCLUSION: More high-quality clinical trials are needed to evaluate the efficacy of add-on treatments for symptom improvement in patients with clozapine resistance. Applying definitions of clozapine resistance would improve the reporting of future clinical trials. Augmentation with second-generation antipsychotics and first-generation antipsychotics can be beneficial, but the supporting evidence is from low-quality studies. Electroconvulsive therapy may be effective for clozapine-resistant positive symptoms.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".