Temperament and Character Profiles and Psychiatric Comorbidities in Patients With Coronary Artery or Valvular Heart Disease: Relationship With Cardiac Disease Severity
Bibliographic record
Abstract
BACKGROUND: We aimed to investigate whether the psychopathological symptoms and temperament-character dimensions observed in patients operated due to coronary artery disease (CAD) or valvular heart disease (VHD) differ among the patients and from healthy individuals. METHODS: Study population was composed of subjects with CAD, VHD and healthy controls (n = 50 in each group). Socio-demographic questionnaire, temperament and character inventory (TCI) and symptom check list-90-R (SCL-90-R) were applied to all groups. Groups were compared about temperament-character dimensions and scores of subscales of SCL-90-R. RESULTS: Harm avoidance was found to be higher in VHD group than those with CAD and, lower in healthy controls than both patient groups (P = 0.004). Reward dependence was similar among both patient groups and, was higher than healthy group (P = 0.015). Depression, anxiety, somatization, obsession and interpersonal sensitivity were found to be similar in both patient groups but they were higher than those in controls (P < 0.001, P < 0.001, P < 0.001, P = 0.002 and P = 0.003, respectively). Phobia was seen equally in CAD group and healthy controls and, was found to be lower in these than in VHD (P = 0.009). Anger score was in descending order in patients with VHD, CAD and healthy controls group (P = 0.010 and 0.001). Paranoia was in descending order in patients with VHD, CAD and controls (P = 0.015 and 0.001). A weak and inverse correlation was found between ejection fraction (EF) and the persistence dimension of temperament scaled by TCI in patients with VHD (r = -0.276, P = 0.052). An inverse correlation was observed between EF and the reward dependence dimension in CAD group (r = -0.195, P = 0.044). In patients with VHD, EF demonstrated an inversely weak (r = -0.289, P = 0.042), moderate (r = -0.360, P = 0.010) and strong (r = -0.649, P < 0.001) correlation with inter-personal sensitivity, phobia and paranoia, respectively. There was an inverse and weak correlation between EF and depression and anger in VHD group (r = -0.302, P = 0.033 and r = -0.240, P = 0.054). CONCLUSION: VHD and CAD exhibit different psychopathological symptoms and temperament traits. There is a correlation between the aforementioned psychopathological symptoms and temperament traits, and EF.
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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) | 0.001 | 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".