Maintenance Pharmacological Treatment of Juvenile Bipolar Disorder: Review and Meta-Analyses
Bibliographic record
Abstract
BACKGROUND: Guidelines for maintenance treatment of juvenile bipolar disorder rely heavily on evidence from adult studies and relatively brief trials in juveniles, leaving uncertainties about optimal long-term treatment. We aimed to systematically review long-term treatment trials for juvenile bipolar disorder. METHODS: We analyzed data recovered by a systematic literature search using the PRISMA guidelines statement, through 2018, for peer-reviewed reports on pharmacological treatments for juvenile bipolar disorder lasting ≥24 weeks. RESULTS: Of 13 reports with 16 trials of 9 treatments (18.8% were randomized and controlled), with 1773 subjects (94.4% BD-I; ages 6.9-15.1 years), lasting 11.7 (6-22) months. Pooled clinical response rates were 66.8% (CI: 64.4-69.1) with drugs vs 60.6% (53.0-66.7) in 3 placebo-control arms. Random-effects meta-analysis of 4 controlled trials yielded pooled odds ratio (OR) = 2.88 ([0.87-9.60], P = .08) for clinical response, and OR = 7.14 ([1.12-45.6], P = .04) for nonrecurrence. Apparent efficacy ranked: combined agents >anticonvulsants ≥lithium ≥antipsychotics. Factors favoring response ranked: more attention deficit/hyperactivity disorder, polytherapy, randomized controlled trial design, nonrecurrence vs response. Adverse events (incidence, 5.50%-28.5%) notably included cognitive dulling, weight-gain, and gastrointestinal symptoms; early dropout rates averaged 49.8%. CONCLUSIONS: Pharmacological treatments, including anticonvulsants, lithium, and second-generation antipsychotics, may reduce long-term morbidity in juvenile bipolar disorder. However, study number, quality, and effect magnitude were limited, leaving the status of scientific support for maintenance treatment for juvenile bipolar disorder inconclusive.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".