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Record W4303613729 · doi:10.3390/brainsci12101342

The Effect of Menopause on Antipsychotic Response

2022· review· en· W4303613729 on OpenAlexaff
Alexandre González-Rodríguez, José Antonio Monreal, Mary V. Seeman

Bibliographic record

VenueBrain Sciences · 2022
Typereview
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEstrogenMenopauseAntipsychoticMedicineSchizophrenia (object-oriented programming)PharmacodynamicsPsychosisPsychologyPharmacologyInternal medicinePsychiatryPharmacokinetics

Abstract

fetched live from OpenAlex

Background: It has been hypothesized that, whenever estrogen levels decline, psychosis symptoms in women increase. At menopause, this can happen in two main ways: (a) the loss of estrogen (mainly estradiol) can directly affect central neurotransmission, leading to increase in schizophrenia-related symptoms, and (b) the loss of estrogen can decrease the synthesis of enzymes that metabolize antipsychotic drugs, thus weakening their efficacy. Aims and Methods: The aim of this narrative review was to investigate the second possibility by searching PubMed and ClinicalTrials.gov for studies over the last two decades that investigated the metabolism of antipsychotics and their efficacy before and after menopause in women or that studied systemic and local estrogen level effects on the pharmacokinetics and pharmacodynamics of individual antipsychotic drugs. Results: The evidence suggests that symptom level in women with schizophrenia rises after menopause for many reasons beyond hormones but, importantly, there is an estrogen-dependent loss of efficacy related to antipsychotic treatment. Conclusion: Effective clinical intervention is challenging; nevertheless, several promising routes forward are suggested.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.189
GPT teacher head0.493
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations25
Published2022
Admission routes1
Has abstractyes

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