Monitoring the Effectiveness of Treatment in Women with Schizophrenia: New Specialized Cooperative Approaches
Bibliographic record
Abstract
Women with schizophrenia have specific health needs that differ from those of men and that change through successive life stages. We aimed to review the biopsychosocial literature on schizophrenia that addresses clinically important questions related to the treatment of women, including somatic morbi-mortality, hyperprolactinemia, comorbid substance use disorders, social risk factors, and medication effectiveness/safety. Data search terms were as follows: (Morbidity AND mortality) OR hyperprolactinemia OR ("substance use disorders" OR addictions) OR ("social risk factors") OR ("drug safety" OR prescription) AND women AND schizophrenia. A secondary aim was to describe a method of monitoring and interdisciplinary staff strategies. Schizophrenia patients show an increased risk of premature death from cardiovascular/respiratory disease and cancer compared to the general population. The literature suggests that close liaisons with primary care and the introduction of physical exercise groups reduce comorbidity. Various strategies for lowering prolactin levels diminish the negative long-term effects of hyperprolactinemia. Abstinence programs reduce the risk of victimization and trauma in women. Stigma associated with women who have serious psychiatric illness is often linked to reproductive functions. The safety and effectiveness of antipsychotic drug choice and dose differ between men and women and change over a woman's life cycle. Monitoring needs to be multidisciplinary, knowledgeable, and regular.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".