Clinical and genetic associations of depressive symptoms in paranoid schizophrenia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".