Augmentation Strategies of Clozapine With Antipsychotics in the Treatment of Ultraresistant Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Approximately 40% to 70% of neuroleptic-resistant schizophrenic patients are nonresponders to clozapine. Several clozapine augmentation strategies have come into clinical practice although often without evidence-based support. Among these strategies, the combined use of clozapine with another antipsychotic has been reported for up to 35% of patients receiving clozapine. OBJECTIVE: The purposes of the present work were to (1) review the available literature on the efficacy and safety of the clozapine augmentation with another antipsychotic using a MEDLINE search of the literature from 1978 to December 2005 and (2) to propose an operational definition of schizophrenia refractory to clozapine ("ultraresistant schizophrenia") for the implementation and homogenization of future therapeutic trials. CONCLUSION: Case controls and open clinical trials largely dominate the literature, and there are only 4 double-blind studies of clozapine augmentation with antipsychotics. The results of these studies are somewhat discrepant. Moreover, the heterogeneity of definitions of resistance to clozapine, of outcome measures and of dose and duration of pharmacological trials is a major limitation for drawing conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".