Connection between alexithymia and chronic diseases of the hearth and lungs
Bibliographic record
Abstract
Alexithymia is manifested by limited abilities to identify and express emotions and is a possible risk factor for the onset and treatment of the disease. To determine the dependence of the presence of alexithymia in patients with chronic lung and heart diseases. One hundred respondents aged 35 to 75 participated in the research, 50% of whom were being treated for chronic lung diseases, and 50% for chronic heart diseases. After filling out the Toronto Twenty-Point Scale (TAS-20) questionnaire, the degree of alexithymia was determined. Using the Chi-square test for independence, it was determined that the presence of alexithymia and chronic heart and lung diseases were dependent characteristics (p > 0.0005), and a significantly larger number of respondents with established alexithymia were treated for chronic lung diseases. Using the Chi-square test, it was shown that the presence of alexithymia and the gender of the subject were not dependent characteristics, while the non-parametric Mann-Whitney U test of significance was used to analyze the dependence of alexithymia and the age of the subject. It demonstrated that there was a statistically significant difference in the age of the subjects with and without alexithymia (p < 0.05). In this paper, we found that alexithymia is a more significant risk factor for the occurrence of chronic impairment of lung function in relation to heart disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".