Mixed states in bipolar and major depressive disorders: systematic review and quality appraisal of guidelines
Bibliographic record
Abstract
OBJECTIVE: This systematic review provided a critical synthesis and a comprehensive overview of guidelines on the treatment of mixed states. METHOD: The MEDLINE/PubMed and EMBASE databases were systematically searched from inception to March 21st, 2018. International guidelines covering the treatment of mixed episodes, manic/hypomanic, or depressive episodes with mixed features were considered for inclusion. A methodological quality assessment was conducted with the Appraisal of Guidelines for Research and Evaluation-AGREE II. RESULTS: The final selection yielded six articles. Despite their heterogeneity, all guidelines agreed in interrupting an antidepressant monotherapy or adding mood-stabilizing medications. Olanzapine seemed to have the best evidence for acute mixed hypo/manic/depressive states and maintenance treatment. Aripiprazole and paliperidone were possible alternatives for acute hypo/manic mixed states. Lurasidone and ziprasidone were useful in acute mixed depression. Valproate was recommended for the prevention of new mixed episodes while lithium and quetiapine in preventing affective episodes of all polarities. Clozapine and electroconvulsive therapy were effective in refractory mixed episodes. The AGREE II overall assessment rate ranged between 42% and 92%, indicating different quality level of included guidelines. CONCLUSION: The unmet needs for the mixed symptoms treatment were associated with diagnostic issues and limitations of previous research, particularly for maintenance treatment.
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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 | 0.036 | 0.151 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".