Gynecological Health Concerns in Women with Schizophrenia and Related Disorders: A Narrative Review of Recent Studies
Bibliographic record
Abstract
Sex and age are important factors influencing physical and mental health in schizophrenia. Our goal was to review the recent literature for associations between gynecological conditions and psychotic illness and to propose integrated strategies for their management in order to improve overall health outcomes in women. We addressed the following questions: What are the prevalence and risk factors of gynecological disorders in women with schizophrenia or delusional disorder (DD)? What are the rates of uptake of gynecological cancer screening and mortality in this population? What role does menopause play? We found an increased incidence of breast cancer in women with schizophrenia. Other gynecological comorbidities were less frequent, but the field has been understudied. Low rates of breast and cervical cancer screening characterize women with schizophrenia. Menopause, because of endocrine changes, aging effects, and resultant comorbidity is associated with high rates of aggressive breast cancer in this population. Uterine and ovarian cancers have been less investigated. Psychosocial determinants of health play an important role in cancer survival. The findings lead to the recommendation that primary care, psychiatry, gynecology, oncology, and endocrinology collaborate in early case finding, in research into etiological links, and in improvement of prevention and treatment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".