Клініко-психопатологічні та патопсихологічні особливості депресивних розладів у хворих на шизофренію
Bibliographic record
Abstract
Analysis of the modern literature on the problem of depressive disorders in schizophrenia structure demonstrated that despite the acceptance of the important role of depressive disorders at schizophrenia there are no single opinion about its place, clinical assessment, therapy, prognostic value for today and the conclusions of different authors on this problem are ambiguous and contradictory that proves that the researches of this direction are timely.Aim of research – to study the clinical-psychopathological and pathopsychological features of depressive disorders in patients with schizophrenia for improvement of diagnostics of this pathology.Materials and methods. 120 patients with schizophrenia and depressive symptomatology in the clinical picture took part in research. They were divided into 3 groups according to the aim of research: 38 patients with schizophrenia with the duration of disease less than 5 years (I group), 39 patients with the duration of disease 7-8 years (II group) and 43 patients with duration of disease more than 13 years (III group).For realization of the aim were used the following methods: clinical-psychopathological that included the assessment of psychic state of patient (collection of complaints, anamnesis, its psychopathological interpretation); Calgary depression scale schizophrenia (CDSS); WHO brief questionnaire of the quality of life (WHOQOL- BREF); questionnaire of the social isolation “The method of evaluation of the suicide risk”, “Self-actualization test” and method of statistical processing of the received data.Results of research. At the research was studied and described the syndrome structure of depression at schizophrenia and pathopsychological features of demonstration of depression at schizophrenia. There was shown that at the different stages of disease depression undergo the significant changes: anxious and obsessive syndrome complexes with time lose its emotional specificity and transform into apathetic-adynamic and anesthetic syndromes. In the process of disease dystrophic and depressive-paranoid syndromes come to the forefront.Conclusions. In the result of research was revealed the negative dynamics of demonstration of the depressive symptoms at schizophrenia, the level of social isolation, suicide risk, life quality, alexithymia and self-appraisal depending on duration of disease
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".