<p>Alexithymia in Patients with Psoriasis: A Cross-Sectional Study from Ecuador</p>
Bibliographic record
Abstract
OBJECTIVE: We designed this study to determine the frequency of alexithymia in Ecuadorian patients with psoriasis, as well as possible associations between demographic factors, disease severity, and treatment adherence. METHODS: A cross-sectional study involving 99 Ecuadorian patients with psoriasis was conducted. Multinomial logistic regressions were performed to ascertain whether age, gender, educational level, years with disease, psoriasis area and severity index (PASI) scores, and treatment adherence categories are prediction factors in patients with psoriasis to present alexithymia, possible alexithymia or no alexithymia. RESULTS: A total of 99 patients participated in the study with a gender distribution of 57.6% male, and an average age and years with disease of 48.3 and 7.4, respectively. Out of all patients, 33.3% presented alexithymia, and 22.2% possible alexithymia, as assessed by the Toronto Alexithymia Scale (TAS-20). The multiple regression model statistically significantly predicted the TAS-20 score from age, gender, educational level, years with psoriasis, PASI score and level of adherence F (7,88) = 4.171, p = 0.001, adj. R2= 0.189. Only having the highest educational level added statistical significance to the prediction of having a lower TAS-20 score, whilst the remainder variables did not. CONCLUSION: We found a similar proportion of alexithymia, as well of average TAS-20 scores among Ecuadorian patients with psoriasis in comparison to previous studies. Only having the highest educational level was found to decrease the TAS-20 score. Age, gender, years with psoriasis, PASI score and level of adherence were not identified as factors that influence the TAS-20 score.
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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 | 0.001 |
| 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) | 0.002 | 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".