Exploring the impact of chronic urticaria profile as a key predictor of alexithymia: A cross‐sectional study
Bibliographic record
Abstract
INTRODUCTION: The relationship between chronic urticaria (CU) and alexithymia, a cognitive-affective impairment characterized by difficulty in identifying and expressing emotions, is complex and underexplored. This study aimed to identify predictors of alexithymia in CU patients by focusing on the impact of coexisting mental illnesses and antihistamine use. METHODS: An online survey was distributed to specialized allergy and dermatology centers from 2021 to 2022. The survey included the TAS-20, UAS-7, UCT, CU-Q2oL, and demographic information. Participants were 18-80 years old, diagnosed with CU, and had no prior diagnosis of alexithymia. The final analysis included a total of 332 respondents from various countries. Regression models were used to investigate the relationship between clinical and demographic factors of patients with CU as key predictors of alexithymia. RESULTS: Among CU patients, the main predictors of having alexithymia were: presenting mental (OR = 2.406, p < 0.05) and cardiovascular comorbidities (OR = 2.085, p < 0.05), active urticaria (as opposed to being urticaria-free), OR = 1.989, p < 0.05, severe impact on quality of life (OR = 1.973, p < 0.01), and the use of oral first-generation antihistamines (OR = 2.340, p < 0.05). The duration of chronic urticaria diagnosis and other types of treatments (sg-AH use, omalizumab use, and corticosteroid use) do not appear to be significantly associated with alexithymia. CONCLUSIONS: Alexithymia is closely linked to clinical and demographic variables among patients with CU. These findings suggest that comprehensive management of CU should include psychological assessment and support, especially for patients with alexithymia and those using fg-AH. Reducing the reliance on fg-AH and addressing mental health issues may improve outcomes for these patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".