The effectiveness of oxytocin in the treatment of stimulant use disorders: a systematic review
Bibliographic record
Abstract
OBJECTIVES: The purpose of this review is to examine human study evidence on the effectiveness of oxytocin in this patient population. Despite stimulant use disorder being a major public health concern, there are no validated pharmacological treatments. Psychosocial interventions show limited effectiveness especially in the more severe cases of stimulant use disorder, whereas animal models suggest that oxytocin may be a useful treatment. METHODS: A literature search using Medline, Embase, and PsychInfo was undertaken. Search results were subsequently imported into Covidence to identify relevant studies. RESULTS: Six studies were included in this review, two of which were pilot studies. Although oxytocin was well tolerated across studies, no study showed a statistically significant reduction in reported cocaine use or cravings. One study suggested oxytocin increased the desire to use cocaine, although the population of participants should be taken into consideration. In contrast, one study showed a trend towards reduced self-reported cocaine use. CONCLUSION: Available research does not support the use of oxytocin in the management of stimulant use disorder; however, included studies are small in sample size and limited in number. There were several noteworthy findings unrelated to this review's primary and secondary outcomes, which are of interest and warrant further research. We provide suggestions for future studies in this area of research. Considering the limited data available at this time, further studies are required before any definitive conclusions can be made regarding the use of oxytocin in stimulant use disorder management.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".