Structure of personal disorders in hypertensive disease patients
Bibliographic record
Abstract
Introduction The strcuture of personal disorders in hypertensive disease aptients remains relevant topic. Objectives The study population included 57 hypertensive disease patients; mean age 49,1+9,6 years old (42 females and 15 males). The control group included 62 healthy individuals (49 females and 13 males); mean age 48,1+8,6 years old. Methods Emotional condition of subjects was assessed using the Depression Scale of Zung, the Spielberger trait Anger scale and Anxiety, the Toronto Alexithymia Scale and SCL – 90-R Questionnaire. Results The study results showed that as compared to the healthy individuals, the hypertensive disease patients showed significantly higher scores of reactive anxiety (46,0+9,0 and 39,0+8,2; р<0,01), personal anxiety (50,3+9,2 and 41,03+7,9; р<0,01), depression (42,7+7,2 and 36,59+5,95;р<0,01), alexithymia (69,4+8,8 and 59,0+9,2; р<0,01), state anger (11,8+3,6 and 10,6+1,8; р<0,01), reactive anger (9,2+2,6 and 8,1+2,4; р<0,05), personal anger (21,4+5,3 and 18,1+4,6; р<0,01), trait anger (8,3+3,0 and 7,3+2,3; р<0,05), self-aggression (16,2+4,9 and 13,4+3,8; р<0,01), aggression towards others (15,9+3,9 and 14,7+3,4; р<0,05), somatization (1,27+0,6 and 0,5+0,4; р<0,01), hostility (1,2+0,7 and 0,5+0,4; р<0,01), obsessive-compulsive traits (1,2+0,7 и 0,6+0,4; р<0,01), psychoticism (0,7+0,6 and 0,27+0,30; р<0,01) and paranoid traits (1,22+0,6 and 0,5+0,4; р<0,01), phobic anxiety (0,6+0,5 and 0,2+0,2; р<0,01) and interpersonal sensitivity (1,2+0,7 and 0,7+0,5; р<0,01). Conclusions Close interrelations between manifestations of anxiety and depression spectrum disorders and anger may be explained by internal conflict between aggressive impulses and the need for adaptive behavior in such individuals, resulting in consistent vicious vortex.
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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 | 0.001 | 0.001 |
| 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) | 0.004 | 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".