Switching From 2 Antipsychotics to 1 Antipsychotic in Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: Antipsychotic polypharmacy (APP) is employed routinely, although it remains controversial because robust evidence supporting its efficacy is lacking. In addition, it is associated with increased costs, higher antipsychotic dosing, and greater risk of side effects. Surprisingly, no prospective, randomized, double-blind studies have addressed this issue; the present investigation set out to fill this gap in knowledge. METHOD: A 12-week, double-blind, randomized, placebo-controlled, single-site study was carried out in individuals with schizophrenia or schizoaffective disorder (DSM-IV) receiving a designated primary antipsychotic plus a secondary antipsychotic, with doses stabilized for each. Individuals were randomly assigned to APP (N = 17), reflecting current treatment, or antipsychotic monotherapy (APM) (N = 18), in which the secondary antipsychotic was discontinued. Assessments occurred weekly during month 1 and every 2 weeks during months 2 and 3; the primary outcome measure was the Brief Psychiatric Rating Scale (BPRS) total score. Other measures included the Clinical Global Impressions (CGI) scale, Simpson-Angus Scale, and Barnes Akathisia Scale. The study was carried out between August 2006 and March 2011. RESULTS: Withdrawal due to clinical deterioration occurred in 1 individual receiving APP (5.8%) and in 4 individuals in the APM group (22.2%). Overall, however, there was no indication of clinical worsening with APM, as measured using BPRS and CGI scale. CONCLUSIONS: Almost 80% (n = 14) of individuals with schizophrenia or schizoaffective disorder currently receiving APP could be safely transitioned to APM with no clinical deterioration. For those who do deteriorate, risk appears greatest in the first several months. From another perspective, results also indicate that a minority of individuals benefit from APP, and research focusing on identifying this group may represent the best strategy to curb excessive use of APP. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00493233.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".