Pharmacotherapies for co‐occurring substance use and bipolar disorders: A systematic review
Bibliographic record
Abstract
OBJECTIVES: Substance use disorders (SUDs), including those for alcohol, stimulants, tobacco, opioids and cannabis, in patients with bipolar disorder are a major clinical and public health problem, and are present in the majority of these patients. Nonetheless, the development of effective pharmacological treatments for co-occurring SUDs in bipolar illness have not been well-developed and may be an important practical reason for the reduced effectiveness of these medications in community practice. METHODS: We conducted a systematic review of the literature (PubMed, Medline, Google Scholar), and identified N = 29 clinical studies, which evaluated both mental health and SUD outcomes in patients with co-occurring bipolar disorders and SUDs. RESULTS: Our findings suggest the potential of valproate sodium and lamotrigine as preferred pharmacological agents for the treatment of co-occurring psychiatric and substance use outcomes in these patients. However, many of the reviewed studies are of open-label designs and of modest sample sizes. CONCLUSIONS: Thus, given the gaps in our knowledge, recommendations for treatment of this common and important co-morbidity are preliminary. Accordingly, the conduct of larger, randomized controlled trials for this co-morbidity is clearly needed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".