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Record W4386168243 · doi:10.3390/brainsci13091238

Monitoring the Effectiveness of Treatment in Women with Schizophrenia: New Specialized Cooperative Approaches

2023· review· en· W4386168243 on OpenAlexaff
M. Natividad, Mary V. Seeman, Jennipher Paola Paolini, A. Balagué, E. Román, N. Bagué, Eduard Izquierdo, Mireia Salvador, Anna Vallet, Anabel Pérez, José Antonio Monreal, Alexandre González-Rodríguez

Bibliographic record

VenueBrain Sciences · 2023
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiopsychosocial modelPsychiatrySchizophrenia (object-oriented programming)MedicinePopulationComorbidityAbstinenceDual diagnosisSubstance abusePsychologyClinical psychology

Abstract

fetched live from OpenAlex

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.185
GPT teacher head0.397
Teacher spread0.211 · 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 designOther design
Domainnot available
GenreReview

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

Citations9
Published2023
Admission routes1
Has abstractyes

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