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Record W2138325415 · doi:10.1177/1078390309350918

Schizophrenia: Women Bear a Disproportionate Toll of Antipsychotic Side Effects

2010· article· en· W2138325415 on OpenAlexaff
Mary V. Seeman

Bibliographic record

VenueJournal of the American Psychiatric Nurses Association · 2010
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsMedicineAntipsychoticPsychiatrySchizophrenia (object-oriented programming)Mental illnessPharmacogenomicsAtypical antipsychoticDosingTollSide effect (computer science)Mental healthInternal medicinePharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: Men and women with schizophrenia suffer not only from their illness but also from the side effects of their medications. OBJECTIVE: To review the toll of antipsychotic side effects specifically on women. STUDY DESIGN: A review of the literature in the PubMed database since 1990 using search terms: sex difference, antipsychotics, schizophrenia, pharmacokinetics, pharmacodynamics, and pharmacogenomics and retrieving additional publications from the reference lists of the original articles. RESULTS: Findings suggest that, because of differing pharmacokinetics, women are more vulnerable than men to weight gain secondary to antipsychotics and to the consequences (metabolic, cardiovascular, reproductive) of weight gain. They are also more vulnerable to hyperprolactinemia and QTc prolongation. CONCLUSIONS: Dosing guidelines need to be critically appraised. The greater toll of side effects in women may undermine adherence to prescribed treatments, add to the stigma that attaches to mental illness, and diminish the quality of women's lives. Side effects increase the cost of mental illness and heighten the burden experienced by caregivers. They exacerbate morbidity and raise mortality rates. They affect the children of women treated with antipsychotic medication.

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)0.001
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 score0.104
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.277
Teacher spread0.274 · 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

Citations39
Published2010
Admission routes1
Has abstractyes

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