Нейрокогнитивный дефицит у больных шизофренией и расстройствами шизофренического спектра вне обострения: возрастной аспект
Bibliographic record
Abstract
Goal of investigation: studying the characteristics of neurocognitivedeficit with regard for the age of onset of disease in patients withschizophre nia and schizophrenia spectrum disorders at initial stages ofdisease and in non-acute condition. Material and instruments: 104 patientswith diagnosed schizophrenia and schizophrenia spectrum disorders, withduration of disease less than five years. The p atients were investigatedusing the Positive and Negative Syndrome Scale (PANSS), the Calgary Depression Rating Scale (CDRS) and also the Brief Assessment of Cognitionin Schizophrenia (BACS). The measur ements were taken in remission and inreverse symptom development phase; the severity did not exceed 70 (PANSSscore). All the patients were divided into three groups: (1) those aged 1 8-29 (N=54), (2) those aged 30-39 (N=26); (3) those aged 40 years, and older(N=24). All the data were collected in one data base and processed using the Statistica for Windows 7 and non-parametric Kolmo gorov-Smirnov test. TheSpearman’s rank correlation coeffi cient (r) was used to assess the relation between the variables. The obtained results seem to support the hypothesis about relationship between the severity of neurocognitive disturbances andthe age of onset of disorder: late manifestation of psychosis happens to beassociated with less prominent neurocognitive deficit that could indicate a decreased psychobiological vulnerability for psychosis.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".