An update on adjunctive treatment options for bipolar disorder
Bibliographic record
Abstract
OBJECTIVES: Bipolar disorder is a complex illness often requiring combinations of therapies to successfully treat symptoms. In recent years, there have been significant advancements in a number of therapies for bipolar disorder. It is therefore timely to provide an overview of current adjunctive therapeutic options to help treating clinicians to inform their patients and work towards optimal outcomes. METHODS: Publications were identified from PubMed searches on bipolar disorder and pharmacotherapy, nutraceuticals, hormone therapy, psychoeducation, interpersonal and social rhythm therapy, cognitive remediation, mindfulness, e-Health and brain stimulation techniques. Relevant articles in these areas were selected for further review. This paper provides a narrative review of adjunctive treatment options and is not a systematic review of the literature. RESULTS: A number of pharmacotherapeutic, psychological and neuromodulation treatment options are available. These have varying efficacy but all have shown benefit to people with bipolar disorder. Due to the complex nature of treating the disorder, combination treatments are often required. Adjunctive treatments to traditional pharmacological and psychological therapies are proving useful in closing the gap between initial symptom remission and full functional recovery. CONCLUSIONS: Given that response to monotherapy is often inadequate, combination regimens for bipolar disorder are typical. Correspondingly, psychiatric research is working towards a better understanding of the disorder's underlying biology. Therefore, treatment options are changing and adjunctive therapies are being increasingly recognized as providing significant tools to improve patient outcomes. Towards this end, this paper provides an overview of novel treatments that may improve clinical outcomes for people with bipolar disorder.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".