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Record W4412402715 · doi:10.1055/a-2579-9406

Is Lumateperone Effective in Bipolar Depression? A Systematic Literature Review and Meta-Analysis on Placebo-Controlled Trials

2025· article· en· W4412402715 on OpenAlexaff
Maurizio Pompili, Mariarosaria Cifrodelli, Maria Anna Trocchia, Ludovica Longhini, Anna Comparelli, Roger S. McIntyre, Isabella Berardelli

Bibliographic record

VenuePharmacopsychiatry · 2025
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPlaceboMeta-analysisRating scaleBipolar disorderClinical Global ImpressionDepression (economics)PsychologyRandomized controlled trialClinical psychologyInternal medicineBipolar II disorderPsychiatryMedicineMoodAlternative medicine

Abstract

fetched live from OpenAlex

Bipolar depression is often difficult to treat and needs a specific therapeutic approach. This systematic review and meta-analysis aimed to evaluate the efficacy of lumateperone to be inclusive of more recently published studies with this agent in depressive episodes of bipolar disorder. Three meta-analyses were conducted to determine whether the mean Montgomery-Asberg Depression Rating Scale (MADRS) values in the placebo groups differ significantly from the mean MADRS scale values in the group receiving lumateperone 42 mg and whether the mean of Clinical Global Impression Bipolar Version - Severity Scale (CGI-BP-S) - (depression subscore and overall bipolar illness) values in the placebo groups differ significantly from the mean CGI-BP-S scale values in the group receiving lumateperone 42 mg. The meta-analysis showed a statistically significant difference between patients treated with lumateperone 42 mg and those treated with placebo for the MADRS and CGI subscores. The clinical profile of lumateperone indicates that it is a established and highly efficacious treatment option for major depressive episodes associated with bipolar disorder.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.837
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.378
Teacher spread0.351 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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 routes1
Has abstractyes

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