Schizophrenia: Women Bear a Disproportionate Toll of Antipsychotic Side Effects
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".