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Record W4411108126 · doi:10.51982/bagimli.1679654

Comparative clinical and sociodemographic assessment of substance use in first episode, drug-naïve psychosis and schizophrenia patients

2025· article· en· W4411108126 on OpenAlexaboutno aff
Demir Faruk Dağ, Özcan Uzun

Bibliographic record

VenueJournal of Dependence · 2025
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosisPsychiatrySchizophrenia (object-oriented programming)DrugSubstance usePsychologyMedicine

Abstract

fetched live from OpenAlex

Objective: This study aims to compare recent-onset, drug-naïve patients with first-episode psychosis (FEP) and patients with schizophrenia in terms of substance and smoking history, and to explore their associations with sociodemographic and clinical characteristics.Methods: A total of 107 patients were included: 56 with drug-naïve FEP and 51 with schizophrenia. Standardized clinical instruments were used, including the Clinical Global Impression–Severity Scale (CGI-S), the Scale for the Assessment of Negative Symptoms (SANS), the Scale for the Assessment of Positive Symptoms (SAPS), and the Calgary Depression Scale for Schizophrenia (CDSS).Results: Substance use was more prevalent among schizophrenia patients (41.2%) compared to FEP patients (25.0%). In both groups, substance use was associated with increased smoking, alcohol consumption, and greater clinical severity. Specifically, FEP patients with substance use reported significantly higher depressive and negative symptoms, as well as greater illness severity. Among schizophrenia patients, substance use was correlated with elevated SAPS, SANS, and CGI-S scores, as well as higher rates of self-mutilation.Conclusion: Substance use contributes to greater symptom burden, behavioral dysregulation, and overall clinical severity in both FEP and schizophrenia. Early screening and the integration of dual-diagnosis treatment strategies are essential to mitigate adverse outcomes in psychotic disorders.

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 score0.009
Threshold uncertainty score0.412

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 studies0.0000.000
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 head0.046
GPT teacher head0.387
Teacher spread0.341 · 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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