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Record W4377089976 · doi:10.4088/jcp.23m14780

Head-To-Head Comparison of Vortioxetine Versus Desvenlafaxine in Patients With Major Depressive Disorder With Partial Response to SSRI Therapy

2023· article· en· W4377089976 on OpenAlexafffund
Roger S. McIntyre, Ioana Florea, Mads Møller Pedersen, Michael Cronquist Christensen

Bibliographic record

VenueThe Journal of Clinical Psychiatry · 2023
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsBrain and Cognition Discovery FoundationUniversity of Toronto
FundersCanadian Institutes of Health ResearchBausch HealthH. Lundbeck A/SNational Natural Science Foundation of ChinaPurdue UniversityNeurocrine BiosciencesSunovionNovo NordiskEisaiSanofiPfizer
KeywordsVortioxetineDepression (economics)Major depressive disorderPsychiatryPsychologyMedicineClinical psychologyCognition

Abstract

fetched live from OpenAlex

To compare the efficacy of vortioxetine and the serotonin-norepinephrine reuptake inhibitor (SNRI) desvenlafaxine in patients with major depressive disorder (MDD) experiencing partial response to initial treatment with a selective serotonin reuptake inhibitor (SSRI). diagnosis of MDD who experienced partial response to SSRI monotherapy. The primary endpoint was mean change from baseline to week 8 in Montgomery-Åsberg Depression Rating Scale (MADRS) total score. Differences between groups were analyzed using mixed models for repeated measures. = .044). Treatment-emergent adverse events (TEAEs) were reported in 46.1% and 39.6% of patients in the vortioxetine and desvenlafaxine groups, respectively; these were mostly mild or moderate in intensity (> 98% of all TEAEs in each group). Compared with the SNRI desvenlafaxine, vortioxetine was associated with significantly higher rates of CGI-S remission, better daily and social functioning, and greater treatment satisfaction in patients with MDD and partial response to SSRIs. These findings support the use of vortioxetine before SNRIs in the treatment algorithm in patients with MDD. ClinicalTrials.gov Identifier: NCT04448431.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.441
Teacher spread0.375 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations33
Published2023
Admission routes2
Has abstractyes

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