Importance of alexithymia on anxiety and depression in alopecia areata: a cohort study
Bibliographic record
Abstract
Abstract Background Alopecia areata (AA) is an autoimmune hair loss disease, considered a psychosomatic disease with comorbid symptoms of depression and anxiety. Alexithymia, defined as difficulties in recognizing and describing feelings, has been found to be a vulnerability factor for developing anxiety and depression and somatic disease. The psychological burden of AA needs to be further investigated in larger studies by using standardized instruments developed to identify alexithymia, and clinical depression and anxiety. The outcome is important when treating patients with AA as well as for decisions on treatment. Objectives To explore the prevalence of alexithymia and its subtypes and how they relate to depressive and anxiety symptoms in patients with AA. Methods In this cohort study 100 patients with AA were interviewed about sociodemographic data, AA disease variables, and previous and present mental health. The Beck Depression Inventory-II (BDI-II), the Beck Anxiety Inventory (BAI) and the Toronto Alexithymia Scale-20 (TAS-20) were used to identify alexithymia, depression and anxiety. Associations between alexithymia and subtype scores – difficulties identifying feelings (DIF), difficulties describing feelings (DDF) and externally oriented thinking (EOT) – were analysed in relation to depression and anxiety scores. Relations between alexithymia, depressive and anxiety scores, and AA and sociodemographic variables were examined. Results Prevalences of depression, anxiety and alexithymia in patients with AA were 16% (n = 16/100), 22% (n = 22/100) and 32% (n = 32/100), respectively. There was a statistically significant relation between DIF and anxiety and between DIF, DDF and depression. Lower levels of education were related to alexithymia, depression and anxiety symptoms. Alexithymia was statistically significantly more frequent among those who were younger at AA onset, in the relapsing form of AA and with nonfamilial AA. Previous mental affective illness was reported in 77% (n = 77/100) of patients with AA during the life course. Conclusions Patients with AA had a higher prevalence of depression, anxiety and alexithymia compared with normative data. Alexithymia was found among those with earlier AA onset, younger age at interview and lower educational levels. Higher EOT scores were found among those with anxiety and lower education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".