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Record W2151784172 · doi:10.1176/appi.ajp.161.8.1324

Gender Differences in the Prescribing of Antipsychotic Drugs

2004· article· en· W2151784172 on OpenAlexaff
Mary V. Seeman

Bibliographic record

VenueAmerican Journal of Psychiatry · 2004
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsAntipsychoticMedicineDosingDrugAdverse effectAntipsychotic drugClinical pharmacologyPharmacodynamicsPregnancyPsychiatryPharmacokineticsSchizophrenia (object-oriented programming)Pharmacology

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this article is to offer recommendations and rationale for gender-specific antipsychotic treatment. METHOD: The author summarizes reviews of recent literature in psychiatric clinical trials, pharmacology, drug safety, toxicology, obstetrics and gynecology, and pediatrics. RESULTS: The pharmacokinetics and pharmacodynamics of antipsychotic drugs differ in women and men and are influenced by gender-specific factors such as body build, diet, smoking, concurrent medication, exercise, substance use, and hormonal transitions. In general, and for some drugs in particular, women require lower doses in order to stay well. Because preliminary drug testing is not done in pregnant women, the issue of effective dosing during pregnancy is unstudied, and safety for fetuses and nursing infants may not become evident until a drug is widely used. Specific adverse effects on issues crucial to women (e.g., parenting) have not been well studied, but some side effects, such as weight gain, passivity, hypotension, and hyperprolactinemia, are reported to be particularly problematic for women. Some serious side effects are more often seen among women than among men. CONCLUSIONS: Optimal maintenance regimens of antipsychotics for women and men are not the same.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.211

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.024
GPT teacher head0.299
Teacher spread0.276 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations218
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of PsychiatrySame topicMaternal Mental Health During Pregnancy and PostpartumFrench-language works237,207