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Record W2550446128 · doi:10.1097/gme.0000000000000772

Treating schizophrenia during menopause

2016· review· en· W2550446128 on OpenAlexaff
Amnon Brzezinski, Noa A. Brzezinski‐Sinai, Mary V. Seeman

Bibliographic record

VenueMenopause The Journal of The North American Menopause Society · 2016
Typereview
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineMenopauseSchizophrenia (object-oriented programming)PsychiatryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this review is to examine three questions: What are the risks and benefits of treating women with schizophrenia with hormone therapy (HT) at menopause? Should the antipsychotic regimen be changed at menopause? Do early- and late-onset women with schizophrenia respond differently to HT at menopause? METHODS: MEDLINE databases for the years 1990 to 2016 were searched using the following interactive terms: schizophrenia, gender, menopause, estrogen, and hormones. The selected articles (62 out of 800 abstracts) were chosen on the basis of their applicability to the objectives of this targeted narrative review. RESULTS: HT during the perimenopause in women with schizophrenia ameliorates psychotic and cognitive symptoms, and may also help affective symptoms. Vasomotor, genitourinary, and sleep symptoms are also reduced. Depending on the woman's age and personal risk factors and antipsychotic side effects, the risk of breast cancer and cardiovascular disease may be increased. Antipsychotic types and doses may need to be adjusted at menopause, as may be the mode of administration. CONCLUSIONS: Both HT and changes in antipsychotic management should be considered for women with schizophrenia at menopause. The question about differences in response between early- and late-onset women cannot yet be answered.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.003
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 head0.029
GPT teacher head0.327
Teacher spread0.299 · 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 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

Citations45
Published2016
Admission routes1
Has abstractyes

Explore more

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