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Record W4205736280 · doi:10.3389/fpsyt.2021.797577

Depression, Estrogens, and Neuroinflammation: A Preclinical Review of Ketamine Treatment for Mood Disorders in Women

2022· review· en· W4205736280 on OpenAlexafffund
Collin Gagne, Alexandre Piot, Wayne G. Brake

Bibliographic record

VenueFrontiers in Psychiatry · 2022
Typereview
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKetamineDepression (economics)AntidepressantNeuroinflammationMoodMood disordersPsychologyPsychiatryHormoneMedicineNeuroscienceInternal medicineAnxiety

Abstract

fetched live from OpenAlex

Ketamine has been shown to acutely and rapidly ameliorate depression symptoms and suicidality. Given that women suffer from major depression at twice the rate of men, it is important to understand how ketamine works in the female brain. This review explores three themes. First, it examines our current understanding of the etiology of depression in women. Second, it examines preclinical research on ketamine's antidepressant effects at a neurobiological level as well as how ovarian hormones present a unique challenge in interpreting these findings. Lastly, the neuroinflammatory hypothesis of depression is highlighted to help better understand how ovarian hormones might interact with ketamine in the female brain.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.336
Teacher spread0.300 · 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 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

Citations18
Published2022
Admission routes2
Has abstractyes

Explore more

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