Worsening stimulant use disorder outcomes coinciding with off-label antipsychotic prescribing: a commonly unrecognised side effect?
Bibliographic record
Abstract
Antipsychotic medications exert their effects via dopamine antagonism and are widely used off-label among persons with substance use disorders (SUD). While dopamine antagonists are recognised to stimulate food craving and weight gain, outside of possibly increasing nicotine craving and use, their impact on other SUD outcomes is poorly recognised. In this context, research has demonstrated that antipsychotic therapy can produce 'supersensitivity' to dopamine, enhancing the motivational effects of addictive drugs. Worsened drug craving and higher rates of substance use have also been observed in double-blind placebo-controlled trials. Nevertheless, widespread off-label antipsychotic prescribing among persons with SUD implies that the risks of worsening SUD outcomes are overall poorly recognised in both primary care and among specialists. We present a typical case of worsening stimulant use disorder in a patient prescribed antipsychotic medication for low mood and insomnia, highlighting that this is likely a widely under-recognised adverse effect of off-label antipsychotic therapy.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Case report | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Case report | low |
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".