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Clinical and genetic associations of depressive symptoms in paranoid schizophrenia

2025· article· W7116922454 on OpenAlexaboutno aff
R. I. Sultanova, V. R. Gashkarimov, K. A. Gasenko, I. S. Efremov, A. R. Asadullin

Bibliographic record

VenueMedical Herald of the South of Russia · 2025
Typearticle
Language
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)Paranoid schizophreniaDepression (economics)Depressive symptomsMajor depressive disorderRating scaleSchizophrenia spectrumPositive and Negative Syndrome Scale

Abstract

fetched live from OpenAlex

Objective : to establish clinical and genetic associations in patients with paranoid schizophrenia with and without depressive manifestations. Materials and methods : patients with paranoid schizophrenia after 10–14 days of inpatient treatment were included. Psychometric methods were used: Calgary Depression Scale for Patients with Schizophrenia (CDSS), PANSS scale, Columbia Suicide Risk Rating Scale (C-SSRS). Genotyping of the HTR2A gene (rs6313) was performed by PCR. Statistical methods: Microsoft Excel, IBM SPSS Statistics 26. Results: depressive manifestations were detected in 31.8% (124/390). The least frequent item was “suicide”, but it was expressed most strongly in a severe degree. Conclusions : 31.8% of respondents with paranoid schizophrenia experienced depressive symptoms. An inverse relationship was found between the severity of the condition and the frequency of depressive symptoms: the most common was the absence of symptoms, while severe symptoms were the least common. Hopelessness demonstrated an association with the HTR2A rs6313 gene (p=0.044). No significant correlations were found when comparing the remaining values on the Calgary Depression Scale.

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)0.004
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 score0.042
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.019
GPT teacher head0.337
Teacher spread0.317 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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