MétaCan
Menu
Back to cohort
Record W4416744877 · doi:10.51731/cjht.2025.1292

Quetiapine for Major Depressive Disorder

2025· article· W4416744877 on OpenAlexfundaboutno aff
CDA-AMC

Bibliographic record

VenueCanadian Journal of Health Technologies · 2025
Typearticle
Language
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsnot available
FundersScience and Technology Department of Zhejiang ProvinceNational Laboratory of Pattern RecognitionU.S. Department of DefenseCanadian Network for Mood and Anxiety TreatmentsChina Postdoctoral Science FoundationU.S. Department of Veterans AffairsWest China Hospital, Sichuan UniversityDepartment of Science and Technology of Sichuan ProvinceNational Natural Science Foundation of China
KeywordsQuetiapineAtypical antipsychoticMajor depressive disorderGuidelineQuetiapine FumarateAnxietySchizophrenia (object-oriented programming)Bipolar disorder

Abstract

fetched live from OpenAlex

What Is the Issue? Quetiapine is a second-generation antipsychotic (SGA) or atypical antipsychotic drug primarily used for treating schizophrenia and bipolar disorder. Quetiapine extended-release is also indicated by Health Canada for the symptomatic relief of major depressive disorder (MDD) when currently available approved antidepressant drugs have failed. Decision-makers are interested in understanding the evidence regarding quetiapine’s clinical effectiveness, safety, and place in therapy relative to other medications for the treatment of MDD. What Did We Do? We searched key resources, including journal citation databases, and conducted a focused internet search for relevant evidence published since 2020. What Did We Find? The evidence suggests that quetiapine and other SGAs (particularly aripiprazole, brexpiprazole, cariprazine, olanzapine, risperidone, and ziprasidone) have similar efficacy but have unique safety profiles when used for the treatment of adults with treatment-resistant MDD. The evidence also suggests that treatment with quetiapine reduced symptoms of treatment-resistant depression compared to treatment with lithium. Evidence-based guidelines recommend using quetiapine as a second-line augmentation treatment option for MDD in patients whose disease had partial or no response to an adequate dose of initial pharmacotherapy. The Canadian Network for Mood and Anxiety Treatments (CANMAT) guideline recommends atypical antipsychotics such as quetiapine in combination with an antidepressant as the first-line treatment for MDD with psychotic features. The Royal Australian and New Zealand College of Psychiatrists (RANZCP) guideline suggests using quetiapine as monotherapy or adjunct therapy for patients with major depression. What Does It Mean? Quetiapine could be used as second-line augmentation treatment for MDD in patients whose disease had partial or no response to an adequate dose and duration of 2 or more antidepressants (i.e., those with treatment-resistant depression or difficult to treat depression). SGAs such as quetiapine, in combination with an antidepressant, may be used as a first-line treatment for MDD that is very severe or has associated psychotic features. Quetiapine may reduce symptoms of depression and improve social functioning compared with lithium. However, patients reported more side effects with quetiapine than lithium and viewed the side effects of lithium as more manageable. Factors such as patient characteristics, patient tolerability, patient values, side effect profiles of different treatment options, and clinician expertise may also be important to guide choice of treatment for MDD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.005

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.

Opus teacher head0.018
GPT teacher head0.318
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Health TechnologiesSame topicTreatment of Major DepressionFrench-language works237,207