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Record W2177328614 · doi:10.1155/2015/674641

Depressive Symptoms during an Acute Schizophrenic Episode: Frequency and Clinical Correlates

2015· article· en· W2177328614 on OpenAlexaboutno aff
Ravi Philip Rajkumar

Bibliographic record

VenueDepression Research and Treatment · 2015
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPositive and Negative Syndrome ScaleSchizophrenia (object-oriented programming)MedicinePsychopathologyDepression (economics)PsychiatryDepressive symptomsClinical psychologyInternal medicineNegative symptomPsychosisAnxiety

Abstract

fetched live from OpenAlex

Introduction. Depressive symptoms are common in schizophrenia and are associated with poorer functioning, lower quality of life, and an elevated risk of suicidal behaviour. There are few studies on the occurrence and correlates of these symptoms in acutely ill patients with schizophrenia. Method. 72 acutely ill patients with schizophrenia were assessed for depression using the Calgary Depression Scale for Schizophrenia (CDSS). A cut-off score of ≥6 on the CDSS was used to identify clinically significant depressive symptoms. The relationship between depression and illness variables, including psychotic symptom dimensions as measured by the Positive and Negative Syndrome Scale for Schizophrenia (PANSS), was examined. Results. Eleven (15.3%) patients had clinically significant depressive symptoms. These patients scored higher on the positive and general psychopathology scales of the PANSS and had higher rates of suicidal behavior and poorer functioning. The severity of depressive symptoms was positively correlated with the PANSS positive subscale and negatively correlated with the PANSS negative subscale. Discussion. These findings confirm previous reports that depressive symptoms in active schizophrenia is related to the severity of positive psychotic symptoms and is a risk factor for suicidal behaviour in these 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.091
GPT teacher head0.422
Teacher spread0.331 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

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