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Record W2795125065 · doi:10.1093/schbul/sby018.1027

S240. DETERMINANT FACTORS OF REAL-WORLD FUNCTIONING IN SCHIZOPHRENIA

2018· article· en· W2795125065 on OpenAlexaboutno aff
Leticia González-Blanco, María Paz García‐Portilla, Leticia García-Álvarez, Lorena de la Fuente-Tomás, Celso Iglesias García, Ana Coto, Julio Bobes

Bibliographic record

VenueSchizophrenia Bulletin · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsnot available
Fundersnot available
KeywordsPositive and Negative Syndrome ScaleInternal medicineConfoundingPsychopathologyPsychologyGlobal Assessment of FunctioningSchizophrenia (object-oriented programming)HamdMedicinePsychiatryClinical psychologyPsychosisAnxiety

Abstract

fetched live from OpenAlex

Negative, cognitive and depressive symptoms, as well as physical comorbidities, have a great impact on the real-world functioning in patients with schizophrenia (SZ) (1, 2, 3). However, not all the studies have employed accurate psychometric instruments to assess these symptoms, nor have all these factors been studied simultaneously. The aim of the current study is to analyze the determinants of functionality in SZ measured by the Personal and Social Performance (PSP) scale, and considering not exclusively psychopathological and cognitive variables, but also aspects related to physical health and inflammation. Sample: 73 outpatients with SZ, duration of illness ≤10 years, under stable maintenance treatment [mean age (31.7 ± 6.5), males (61.6%)]. Clinical variables: PANSS, CGI-Severity, Clinical Assessment Interview of Negative Symptoms (CAINS) -Motivation/Pleasure (MAP) & Expression (EXP) domains-, Brief Negative Symptom Scale (BNSS), Calgary Depression Scale (CDS), MATRICS Consensus Cognitive Battery (MCCB), PSP. Biological variables: Glucose, cholesterol, LDL, HDL, triglycerides, TSH, prolactin, insulin, uric acid, alkaline phosphatase (APh), C-reactive protein (CRP), TNF-α, interleukin(IL)-6, IL-2, IL-1β, IL-1RA, homocysteine, HT (% hemolysis), lipid peroxidation (LPO), catalase. Pearson correlations were performed to select variables significantly related to PSP scores which were later included in stepwise multiple linear regression analyses. Age, sex, education, smoking, alcohol use, BMI, antipsychotic equivalent doses and other confounding factors were considered. Final model for PSP total score (R2=0.778, F=45.564, p<0.001) identified that CGI-Severity (β= -0.279), PANSS-NM (negative Marder Factor) (β = -0.218), Asociality subscale of BNSS (β= -0.383) and IL-2 (β= -0.269) were significant predictors. Predicting variables included in regression models for specific PSP domains: - Self-care (R2=0.661, F=22.947, p<0.001): PANSS-NM (β=0.458), Avolition subscale of BNSS (β=0.248), IL-2 (β=0.221), APh (β=0.201). - Useful activities (R2=0.563, F=42.473, p<0.001): CGI-S (β=0.245), Avolition (β=0.557). - Social relationships (R2=0.731, F=56.998, p<0.001): Asociality (β=0.578), PANSS-GP (β=0.276), CAINS-EXP (β=0.178). - Aggresive behaviour (R2=0.335, F=16.892, p<0.001): PANSS-P (β=0.408), CDS (β=0.273). 1.Negative symptoms are the most important determinants of a deficit in the real-world functioning in SZ, especially “asociality.” 2.“Apathy” has a negative impact on self-care and useful activities domains. 3.Proinflammatory cytokine IL-2 marks poor functionality. 1. Harvey (2014). Assessing disability in schizophrenia: tools and contributors. J Clin Psychiatry. 2. Strassnig et al. (2015). Determinants of different aspects of everyday outcome in schizophrenia: The roles of negative symptoms, cognition, and functional capacity. Schizophr Res. 3. Menendez-Miranda et al. (2015). Predictive factors of functional capacity and real-world functioning in patients with schizophrenia. Eur Psychiatry.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.029
GPT teacher head0.264
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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