Practitioner Review: The effects of atypical antipsychotics and mood stabilisers in the treatment of depressive symptoms in paediatric bipolar disorder
Bibliographic record
Abstract
BACKGROUND: The management of depressive and mixed symptoms in children and adolescents with bipolar disorder (BD) remains a matter of debate. The goal of this review is, thus, to systematically examine the impact of atypical antipsychotics (AAPs) and mood stabilisers in the treatment of bipolar depression and/or mixed states. METHODS: A literature search was conducted for studies assessing the efficacy of pharmacological treatments for bipolar disorder type I, type II and not otherwise specified with a recent depressive, mixed or manic episode (with depressive symptoms) following DSM-IV criteria in children and adolescents as either acute or maintenance treatment. The databases searched were PubMed/Medline, Google Scholar and Tripdatabase, as well as ClinicalTrials.gov. The search was limited to clinical trials, systematic reviews, meta-analyses and open-label trials published in the English language between the years 2000 and 2015. Sixty clinical studies were found assessing the efficacy of mood stabilisers and AAPs in paediatric BD. Fifteen studies were not included in the primary analysis because they did not assess depressive symptomology/include scores on rating scales of depressive symptoms (Online Supplementary Material). RESULTS: There is sufficient evidence for a Grade A recommendation of the use of olanzapine plus fluoxetine at reducing depressive symptoms in bipolar depression and of quetiapine at high doses for depressive symptoms occurring during mixed episodes. Importantly, even though monotherapy with aripiprazole, risperidone, valproate and lithium was effective at controlling mania, these drugs were not effective at reducing depressive symptoms (level A evidence for nonrecommendation). CONCLUSIONS: These results mostly overlap with the approved treatments for bipolar depression in adults.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| 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".