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Negative Symptoms And Body Mass Index In The Chronic Phase Of Schizophrenia

2017· other· en· W6908634534 on OpenAlexaboutno aff

Bibliographic record

VenueBiblioBoard Library Catalog (Open Research Library) · 2017
Typeother
Languageen
FieldArts and Humanities
TopicHistory of Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)Body mass indexPositive and Negative Syndrome ScaleAnalysis of varianceDepression (economics)Negative symptomScale (ratio)

Abstract

fetched live from OpenAlex

Background and Aims:Our study aimed to validate the hypothesis that negative symptoms of schizophrenia encompass two domains namely apathy-avolition (AA) and diminished expression (DE) and to investigate the relationship of these domains with body mass index.Methods:A total of 100 consecutive schizophrenia outpatients, with primary negative symptoms, were evaluated using the Positive and Negative Syndrome Scale (PANSS), Negative Symptoms Assessment Scale (NSA-16), Calgary Depression Scale for Schizophrenia (CDSS), Simpson-Angus Scale (SAS) and a semi-structured interview was used to assess demographic features and body mass index. Data were analyzed using descriptive statistics, principal component analysis,correlation analysis and analyses of variance and co-variance.Results:We found a two-factor solution for the negative symptoms of schizophrenia represented by AA and DE. Analyses of correlation, variance and co-variance suggested that higher AA scores were associated with normal weight. No significant differences were found regarding DE scores in relationship with the body mass index.Conclusions:Our findings suggest the AA and DE domains show meaningful differences concerning the relationship with body mass index. Lower apathy-avolition levels are associated with higher body mass index in chronic stable schizophrenia patients.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.001
Science and technology studies0.0010.005
Scholarly communication0.0020.005
Open science0.0050.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0150.000

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 head0.081
GPT teacher head0.350
Teacher spread0.268 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2017
Admission routes1
Has abstractyes

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