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Record W2408055922 · doi:10.3810/pgm.11.1999.suppl1.4

Venlafaxine versus SSRI Therapy for Major Depression

2010· article· en· W2408055922 on OpenAlexaff
David Bakish

Bibliographic record

VenuePostgraduate Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsVenlafaxineMedicineCitalopramAntidepressantClomipramineParoxetineDepression (economics)PlaceboOnset of actionMajor depressive disorderPsychiatryInternal medicineAnxietyAlternative medicineMood

Abstract

fetched live from OpenAlex

Although any effective antidepressant will produce a therapeutic response in 50% to 60% of patients with major depression, this means that the needs of the remaining 40% to 50% of patients go unmet. Consequently, selection of optimal antidepressant therapy for major depression should entail the consideration of drugs that have the greatest potential to induce a therapeutic response, remissions and, ultimately, long-term recovery. According to findings from various clinical studies, antidepressants with multiple mechanisms of action (eg, venlafaxine, clomipramine) yielded higher response rates than did other antidepressants (eg, paroxetine, citalopram). Moreover, results of placebo-controlled and active drug-controlled clinical studies have demonstrated that compared with the SSRIs, the combined-action antidepressant venlafaxine exhibited a more robust efficacy in that it consistently produced higher response and remission rates.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.044
GPT teacher head0.348
Teacher spread0.304 · 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 designBench or experimental
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

Citations1
Published2010
Admission routes1
Has abstractyes

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