Multi-Method Approach in the Assessment of Alexithymia in Patients With Scleroderma: Use of Two Different Tools
Bibliographic record
Abstract
Objective: Alexithymia is frequent in patients with some chronic dermatological conditions. The aim of the study was to measure the prevalence of alexithymia in patients with systemic sclerosis (SSc) using two different tools. Methods: Consecutive patients diagnosed with SSc were recruited at day hospital and hospitalization regimen. Alexithymia was measured using the self-administered questionnaire 20-item Toronto Alexithymia scale (TAS-20) and the semi-structured interview 24-item Toronto Structured Interview for Alexithymia (TSIA). Results: The study sample consisted of 67 female patients, aged from 29 to 82 years. According to TAS-20, 22.4% of patients were alexithymic and 17.9% were borderline alexithymic. Also, in our sample mean TAS-20 value was 48.9 and TSIA mean value was 20.3. Spearman’s correlation coefficient between TAS-20 total score and TSIA total score was 0.603. A high correlation was observed between TAS total score and the “Affective Awareness” (AA) scale of the TSIA. TSIA total score significantly correlated with the “Difficulty describing feeling to others” (DDF) scale of the TAS-20. The highest correlation between scales of the two instruments was that between TAS-20 DDF and TSIA AA (r = 0.675). Conclusion: The prevalence of alexithymia in SSc patients was higher than in the general population, and similar to that of psoriasis patients. The measurements obtained using the TAS-20 and the TSIA were highly correlated. However, they also showed some diversities in the scales, indicating that they may measure different domains.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".