Review of Evidence for Use of Antidepressants in Bipolar Depression
Bibliographic record
Abstract
OBJECTIVE: Depressive episodes predominate over the course of bipolar disorder and cause considerable functional impairment. Antidepressants are frequently prescribed in the treatment of bipolar depression, despite concerns about efficacy and risk of switching to mania. This review provides a critical examination of the evidence for and against the use of antidepressants in bipolar depression. DATA SOURCES: English-language peer-reviewed literature and evidence-based guidelines published between January 1, 1980, and March 2014, were identified using PubMed, MEDLINE, PsycINFO/PsycLIT, and EMBASE. All searches contained the terms antidepressants, bipolar depression, depressive episodes in bipolar disorder, and treatment guidelines for bipolar depression. Meta-analyses, randomized controlled trials, systematic reviews, and practice guidelines were included. Bibliographies from these publications were used to identify additional articles of interest. DATA EXTRACTION: Studies involving treatment of bipolar depression with antidepressant monotherapy, adjunctive use of antidepressant with a mood stabilizer, and meta-analysis of such studies combined were reviewed. CONCLUSIONS: The body of evidence on the use of antidepressant monotherapy to treat patients with bipolar depression is contentious, but the recommendations from evidence-based guidelines do not support antidepressant monotherapy for bipolar depression. Only when mood stabilizer or atypical antipsychotic monotherapy has failed should adjunctive treatment with an antidepressant be considered.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 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) | 0.007 | 0.001 |
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".