MétaCan
Menu
Back to cohort
Record W2006321561 · doi:10.1517/14728210903107751

Emerging drugs for major depressive disorder

2009· review· en· W2006321561 on OpenAlexaff
Sidney H. Kennedy, Sakina J. Rizvi

Bibliographic record

VenueExpert Opinion on Emerging Drugs · 2009
Typereview
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsAgomelatineAntidepressantMonoamine neurotransmitterMedicineNorepinephrineDopamineSerotoninPharmacologyDuloxetineMelatoninNeurosciencePsychiatryReceptorInternal medicinePsychologyAnxiety

Abstract

fetched live from OpenAlex

Most current pharmacologic antidepressant treatments target either or both serotonin and norepinephrine systems in the brain to alleviate depressive symptoms. However, > or = 30% of patients with major depressive disorder fail to respond to these antidepressants, inciting a need for alternative treatment strategies. In the past decade, there has been extensive research into improving the mechanism of action for monoamine agents as well as identifying novel treatment targets for depression. For monoamines, drugs that increase serotonin, norepinephrine, melatonin or dopamine have been explored as putative antidepressants and in some cases approved (agomelatine and desvenlafaxine). Novel drugs that act on amino acid receptors, neurotrophic factors, cytokines, neuropeptides and acetylcholine are also in development. This review will discuss the scientific rationale for these targets, as well as highlight the current status of drugs in development.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.949
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.036
GPT teacher head0.386
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; both teacher heads agree on what is shown here.

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

Citations31
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueExpert Opinion on Emerging DrugsSame topicTreatment of Major DepressionFrench-language works237,207