Mind–Body Connections: Gender differences in Pain Perception, Anxiety Sensitivity, and their Impact on Cardiac Health
Bibliographic record
Abstract
Objectives: The study aimed to identify and analyze gender-specific patterns in the perception of pain and anxiety sensitivity and how they interact to affect health-related quality of life (HRQoL) in myocardial infarction (MI) patients. Materials and Methods: 68 acute MI patients presented within 3 months of the cardiac event during the time span from May to September 2024 were included. They were assessed using Anxiety Sensitivity Index-3, Fear of Pain Questionnaire-III, McGill Pain Questionnaire-Short Form, and Short Form-36 Health Survey. Independent samples t-test was used to analyze the gender differences and Cohen’s d as a measure of effect size. Pearson correlation coefficient and linear regression analysis were done to assess the relationship between variables and to ascertain the predicted variance toward HRQoL. Results: Anxiety sensitivity and fear of pain were higher in females. Women reported increased perception of sensory and affective pain. HRQoL was found to be higher in males. Anxiety sensitivity played a statistically significant role in the prediction of the HRQoL. Conclusion: This study highlights the presence of significant differences in the anxiety sensitivity, pain perception, and fear of pain among male and female acute MI patients. Our findings suggest a significant role of anxiety sensitivity in mediating the quality of life in MI patients post the cardiac event. This may be a result of a unique combination of several biological, psychological, or sociocultural factors which tend to differ by gender and therefore require an individualized approach to assessment and comprehensive management.
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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 | 0.001 | 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".