Psychostimulant Substitution Therapy for the Treatment of Stimulant Use Disorders in Patients with Schizophrenia or Schizoaffective Disorder: A Systematic Review
Bibliographic record
Abstract
Objective: Co-occurrence of schizophrenia/schizoaffective disorder (SSD) and stimulant use disorder (StUD) is an ongoing clinical problem and can lead to poor outcomes. Although emerging evidence has suggested psychostimulant substitution therapy may result in improved outcomes in those with StUD, the efficacy and safety of psychostimulant substitution therapy for StUD in those with concurrent SSD is uncertain. This review aims to systematically find and assess all available efficacy and safety evidence on the use of prescription psychostimulants in those with co-occurring SSD and StUD. Methods: Electronic searches of MEDLINE, PsycINFO, Embase, Scopus, ClinicalTrials, EU Clinical Trials, and CADTH were conducted from inception to February 27, 2024. Any study design was accepted if they involved the following concepts 1) SSD and StUD and 2) prescription psychostimulants. Given the paucity of trials meeting criteria, outcomes of interest were described qualitatively. Risk of bias was assessed using Q-Coh and ROB2. Results: Only seven articles met criteria, and most of these were case reports and series. The single RCT included was at high risk of bias. Outcomes included abstinence, reductions in non-prescribed stimulant use, psychiatric hospitalizations, levels of craving, improvements in mental health, improvements in psychosocial functioning, adherence to antipsychotic medications, and retention in treatment. Most of the results indicated that psychostimulant substitution therapy in individuals with SSD-StUD was not associated with improved outcomes. Conclusion: Available evidence for treatment of StUD via psychostimulant substitution therapy in individuals with SSD is lacking. More exploration is required for this clinical question to allow for current practice to be backed by evidence.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 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) | 0.000 | 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 teacher head, 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".