Alexithymia in Adolescents with Acne: Association with Quality of Life Impairment and Stigmatization
Bibliographic record
Abstract
Alexithymia seems to be more common among patients with skin diseases. However, studies on acne patients are very limited. We conducted this study to evaluate alexithymia in adolescents with acne. In our cross-sectional study, 730 high school students (mean age: 17.05 ± 1.18 years) were recruited. The Toronto Alexithymia Scale (TAS-20) was used to measure alexithymia, the Dermatology Life Quality Index (DLQI) was employed to study quality of life (QoL), and the 6-item Stigmatization Scale (6ISS) was used to evaluate the level of stigmatization in acne subjects. Alexithymia was found in 31% of adolescents, with similar prevalence among those with and without acne (31.3% and 30.1%, respectively). The mean scoring on the TAS-20 in patients with acne (53.1 ± 12.8 points) was not significantly different from that of the non-acne group (53.5 ± 11.9 points). However, significant correlations between TAS-20 scores and QoL assessments (r = 0.332, p < 0.001) as well as stigmatization level (r = 0.284, p < 0.001) were found. These correlations were also significant for the domains of alexithymia described as difficulty in identifying feelings (DIF) and difficulty in describing feelings (DDF), but not for externally oriented thinking (EOT). The findings clearly showed that acne does not predispose to alexithymia; however, alexithymia in acne subjects is related to impaired QoL and stigmatization.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".