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Record W2048646525 · doi:10.1097/yco.0b013e3283297127

Emergent research in the cause of mental illness in women across the lifespan

2009· review· en· W2048646525 on OpenAlexaff
Simone N. Vigod, Donna E. Stewart

Bibliographic record

VenueCurrent Opinion in Psychiatry · 2009
Typereview
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsUniversity Health NetworkWomen's College HospitalUniversity of Toronto
FundersEli Lilly and Company
KeywordsMental illnessPsychologyPsychiatryClinical psychologyMedicineMental health

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: In recent years, there has been an increased appreciation of gender and sex differences in mental illness. This perspective has included attention to sex differences in neurobiology, neurochemistry, sex steroids, endocrine sex reactivity and psychosocial stressors. However, emerging research investigating gene-environment interactions presents another layer of complexity in understanding sex differences in epidemiology, clinical features and treatment of mental disorders across the lifespan. RECENT FINDINGS: The main themes in the current literature point to gene-environment interactions underlying sex-specific differences in the psychiatric sequelae of both early childhood and current life stress. Evidence related to the serotonin-linked polymorphic region (5HTTLPR) polymorphism is strongest, but evidence exists for other candidate genes. There is also emerging support for genetic factors that increase susceptibility of some women to hormonal changes of the reproductive life cycle. The interaction of these genetic factors with various environmental stressors, many of which are more common in women, may increase the risk of mental illness, especially mood disorders. SUMMARY: Further research into sex-specific gene-environment interactions across the lifespan is needed with the goal of improving preventive efforts and optimizing treatment in women's mental health.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.002
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 head0.268
GPT teacher head0.517
Teacher spread0.249 · 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 designOther design
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

Citations41
Published2009
Admission routes1
Has abstractyes

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