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Record W3160232131 · doi:10.20471/acc.2020.59.04.06

Factors Associated with Depression in Patients with Schizophrenia

2020· article· en· W3160232131 on OpenAlexaboutno aff
Boris Golubović

Bibliographic record

VenueActa Clinica Croatica · 2020
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)Depression (economics)LonelinessPositive and Negative Syndrome ScalePsychopathologyPsychiatryClinical psychologyMedicinePsychologySocial isolationInternal medicinePsychosis

Abstract

fetched live from OpenAlex

The aim of this study was to analyze risk factors present in schizophrenic patients with depressive symptomatology. The sample comprised of 76 respondents diagnosed with schizophrenia. In the study, we used the Positive and Negative Syndrome Scale (PANSS) and Calgary Depression Scale for Schizophrenia. The prevalence of depression was estimated to be 30%. The mean scores on the negative subscale of the PANSS were significantly higher in patients with schizophrenia and depression compared to control group (U=3.64, p=0.00), and so were those on the General Psychopathology Scale (U=4.91, p=0.00). Socio-demographic factors were identified as important factors (p<0.05). Personal and environmental factors such as loneliness, immediate social environment, social support and isolation were statistically significantly different between the groups (p<0.05). There was a correlation of poor compliance with psycho-pharmacotherapy, increased number of hospitalizations and shorter remission period with the severity of clinical presentation (p<0.05). Since the presence of these factors is associated with depression in schizophrenia, their early detection in clinical practice is vital to ensure timely prevention of the development of depressive symptomatology.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

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

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 head0.059
GPT teacher head0.306
Teacher spread0.247 · 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 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".

Quick stats

Citations13
Published2020
Admission routes1
Has abstractyes

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