Somatic Augmentation Strategies in Clozapine Resistance-What Facts?
Bibliographic record
Abstract
BACKGROUND: Polypharmacy without evidence-based support is sometimes needed for patients treated with 40% to 70% clozapine who are clozapine nonresponders. Several somatic augmentation strategies are proposed in the scientific literature, with different levels of evidence for safety and efficacy. OBJECTIVES: The purpose of the present study is to review the available literature on the efficacy and safety of clozapine augmentation with somatic agents other than antipsychotics. The following classes of agents are considered: (1) mood stabilizers, (2) antidepressants, (3) electroconvulsive therapy and repetitive transcranial magnetic stimulation, (4) glutamatergic agents, (5)fatty acids supplements, and (6) benzodiazepines. RESULTS: Case controls and small-size clinical trials largely dominate the literature, limiting the power to draw conclusions concerning safety issues and the meaning of negative studies. Moreover, variable definitions of clozapine resistance, heterogeneous outcome measures, and short duration of treatment trials are additional limitations. CONCLUSION: Generally, adjunctive strategies for clozapine-resistant patients remain based on scarce evidence of efficacy and significant safety concerns. Low-frequency repetitive transcranial magnetic stimulation, fatty acids supplements, and mirtazapine showed good tolerability and some efficacy, but the results need replication.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".