Combination of Clozapine With Long-Acting Injectable Antipsychotics in Treatment-Resistant Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Clozapine is indicated for treatment-resistant schizophrenia (TRS), but only 30%-60% of patients will respond. There have been studies of clozapine augmentation with oral second-generation antipsychotics with mixed results, but no studies considering the combination with long-acting injectable antipsychotics (LAIAs). This study is the first to attempt to establish the benefits of the combination of clozapine and LAIAs in TRS using a variety of outcome measures of symptomatology and quality of life. METHODS: A mirror-image study design was employed to review outcome measures 2 years pre and post combination of clozapine with a LAIA in a small sample of patients with chronic schizophrenia or schizoaffective disorders followed by the assertive community treatment service in the community. Outcome measures include demographic data, Brief Psychiatric Rating Scale, Clinical Global Impressions Scale-Improvement and Severity, 24-item Behavior and Symptom Identification Scale, World Health Organization Quality of Life Scale, Health of the Nation Outcome Scales, Threshold Assessment Grid, number of admissions, emergency department (ED) visits, and hospital bed days. RESULTS: Paired sample t tests showed a statistically significant reduction in average ED visits and hospital admissions in the 2 years post combination, with an average 1.8 fewer ED visits (95% CI, 0.58-3.02, P = .024) and a mean reduction of 0.85 hospital admissions (95% CI , 0.363-1.337, P = .008). The reduction in hospital bed days post combination was not statistically significant. Chart reviews found insufficient data for analysis of the remaining outcome measures. CONCLUSIONS: The combination of clozapine and a long-acting injectable antipsychotic appears to reduce health care utilization in terms of ED visits and number of hospital admissions. Larger prospective studies will be required to confirm the results.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".