Quetiapine: novel uses in the treatment of depressive and anxiety disorders
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Quetiapine, an atypical antipsychotic, has been approved for the treatment of schizophrenia, acute mania, bipolar depression and unipolar major depression. However, it is often used (off-label) to treat other depressive disorders and anxiety disorders in children and adults. AREAS COVERED IN THIS REVIEW: This article reviews the evidence for the safety and efficacy of quetiapine in these populations, as both monotherapy and augmentation to other psychotropics. WHAT THE READER WILL GAIN: This article provides an in-depth review of the published literature on the topic and also provides recommendations for use. TAKE HOME MESSAGE: There is strong evidence to support the use of quetiapine in major depressive and generalized anxiety disorders, and preliminary support for treatment-resistant and psychotic depression. There is reasonable evidence of its benefits as an augmenting agent in obsessive-compulsive disorder, while data in other anxiety disorders are limited but promising. While long-term tolerability data are limited, quetiapine appears well tolerated in the short-term. Further randomized controlled trials are needed to confirm the efficacy and tolerability of quetiapine, both short- and long-term, in many of these conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".