Asthma - is there an association between the quality of life and the levels of alexithymia?
Bibliographic record
Abstract
In psychological theories, difficulties of affect regulation, defined as alexithymia, are correlated with asthma but the association between alexithymia, asthma and quality of life has not been explained yet. The aim of the study was to: 1) determine the prevalence of alexithymia in patients with asthma, 2) evaluate the associations between the quality of life and the levels of alexithymia. Methods: Alexithymia was assessed with the TAS- 20 (Toronto Alexithymia Scale). The prevalence of this disorder in patients with asthma was compared to that in healthy subjects. Quality of life was evaluated with: AQLQ by Juniper for asthmatics. The associations among alexithymia, and the quality of life were estimated by data analysis. The data were analyzed using Pearson correlations, t-Student test. A p value ≤ 0.05 was required for statistical significance. Results: Fifty healthy people and fifty one asthmatic outpatients of Military Institute of Medicine in Warsaw, Poland participated in the study. Twenty percent of asthmatics and only four percent of healthy people reported high alexithymia scores. A higher alexithymia score was associated with worse quality of life. Conclusions: The prevalence of alexithymia is higher in patients with asthma. The coexistence of asthma and alexithymia is associated with deterioration of patient's quality of life.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".