A specialized unit for women with schizophrenia: Results from the healthcare model Observatories-Monitoring Stations and Interventions.
Bibliographic record
Abstract
Introduction There are many theoretical reasons to implement gender-specific care for schizophrenia. For all these reasons, the Mutua Terrassa-Functional Unit for Women with Schizophrenia was inaugurated in January 2023 in the context of a community mental health service. Objectives Our aim today is to describe the health care model applied in this newly initiated unit. Methods We created a healthcare model in our new unit consisting of A)Five observatories of Health (somatic morbi-mortality, hyperprolactinemia-HPRL, substance use disorders, social exclusion/discrimination, and drug safety); B)Monitoring stations or vigilance teams (reflecting the 5 observatories); and C)resulting actions (specific interventions). The observatory teams each meet monthly. In this presentation, according to the healthcare model we implemented, we first describe data about the original patient recruitment and then focus on the observatories of somatic morbi-mortality and hyperprolactinemia. Results From 265 potentially eligible women, 42 were included in the 5 observatories. (A) of the 11 women in the observatory of somatic morbi-mortality, 10 women had died within the last 24 months. Causes of Death: (1)respiratory tract disease (n=5,45.4%), (2)cancer (n=3;27.3%): lung cancer (n=1), pancreatic cancer (n=1), kidney cancer (n=1), (3)ischemic colitis (n=1;9%), (4)Alzheimer disease (n=1;9%). 2) Morbidity. One woman had an ongoing glioblastoma. (B)Observatory of HPRL. Eight women with moderate/severe HPRL were included. Strategies for lowering prolactin levels were discussed with neuroendocrinologists. Interventions:adjunctive aripiprazole (n=3), switch to aripiprazole (n=2), lowering antipsychotic doses (n=2), and adjunctive cabergoline (n=1). Conclusions Designating special teams to focus on specific problems of women with schizophrenia will reduce morbidity and improve outcomes in this vulnerable population. Disclosure of Interest None Declared
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".