Clozapine augmentation strategies – a systematic meta-review of available evidence. Treatment options for clozapine resistance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it