Psychosemantics of Pain in Patients With Coronary Artery Disease
Bibliographic record
Abstract
Introduction It is known for a fact that a number of psychological factors may affect heart pain perception in patients with coronary artery disease (CAD). However, psychosemantics of pain in CAD patients was hardly ever explored. Objective To study the features of pain psychosemantics in CAD patients. Methods There were applied McGill Pain Questionnaire (Melzack, 1975); the psychosemantic technique “Classification of sensations” (Tkhostov, Efremova, 1989); the 20-item Toronto Alexithymia Scale (Bagby, Taylor, Parker, 1994); and State-Trait Anxiety Inventory (Spielberger et al., 1983). Fifty-four CAD patients took part in the study, the mean age was 55.9 ± 7.6 years. CAD duration was 5.8 ± 2.6 years. Results CAD patients with the high level of trait anxiety (28%) choose greater variety of descriptors for pain definition, revealing an impaired ability to differentiate between emotional states and physical sensations. They show higher scale values for McGill Pain Questionnaire. Patients with high indices of alexithymia (31%) require significantly fewer words for description of painful and dangerous perceptions within the “Classification of sensations”. This may testify to a certain bafflement in verbal description of the pain. With that, intensity of alexithymia does not correlate significantly with the high level of state and trait anxiety (P > 0.05). The method of “Classification of sensations” revealed that patients with trait anxiety, as well as those with alexithymia, define the pain with significantly more numerous metaphorical and affective descriptors (Pervichko, Zinchenko, 2013). Conclusions Received results prove an important role of psychological factors in etiology of chest pain in CAD patients with the high level of trait anxiety and alexithymia, which supports the urgency of psychotherapy for them. References not available. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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".