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Ketamine Treatment in Depression: A Systematic Review of Clinical Characteristics Predicting Symptom Improvement

2020· review· en· W3018703129 on OpenAlexaff
Darby J. E. Lowe, Daniel J. Müller, Tony P. George

Bibliographic record

VenueCurrent Topics in Medicinal Chemistry · 2020
Typereview
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsKetamineAntidepressantDepression (economics)Context (archaeology)MedicineRandomized controlled trialPopulationPsychiatryClinical psychologyMajor depressive disorderClinical trialTreatment-resistant depressionPsychologyIntensive care medicineInternal medicineCognition

Abstract

fetched live from OpenAlex

Ketamine has been shown to be efficacious for the treatment of depression, specifically among individuals who do not respond to first-line treatments. There is still, however, a lack of clarity surrounding the clinical features and response periods across samples that respond to ketamine. This paper systematically reviews published randomized controlled trials that investigate ketamine as an antidepressant intervention in both unipolar and bipolar depression to determine the specific clinical features of the samples across different efficacy periods. Moreover, similarities and differences in clinical characteristics associated with acute versus longer-term drug response are discussed. Similarities across all samples suggest that the population that responds to ketamine's antidepressant effect has experienced chronic, long-term depression, approaching ketamine treatment as a "last resort". Moreover, differences between these groups suggest future research to investigate the potential of stronger efficacy towards depression in the context of bipolar disorder compared to major depression, and in participants who undergo antidepressant washout before ketamine administration. From these findings, suggestions for the future direction of ketamine research for depression are formed.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.401
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.001
Bibliometrics0.0000.000
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 head0.089
GPT teacher head0.441
Teacher spread0.352 · 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.

Study designSystematic review
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

Citations5
Published2020
Admission routes1
Has abstractyes

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