Alexithymia in Systemic Lupus Erythematosus Patients
Bibliographic record
Abstract
This study aimed to determine the prevalence of alexithymic characteristics in patients with systemic lupus erythematosus (SLE), comparing them with a population of healthy subjects. Fifty-three SLE patients [American College of Rheumatology (ACR) criteria] and 31 healthy volunteer subjects were administered validated scales for alexithymia (Toronto alexythimia scale-20), psychopathology (brief symptom inventory; hospital anxiety and depression scale), personality dimensions (NEO five-factor inventory), and quality of life (short form-36 health survey). The SLE patient's clinical and laboratory evaluations were performed by computerized indicators of activity (SLE disease activity index), of accumulated damage (Systemic Lupus International Collaborating Clinics/American College of Rheumatology damage index), length of disease, and therapy. A high prevalence of alexithymia was found in SLE patients. Alexithymia was associated with psychopathology, personality, and quality of life dimensions. Clinical variables and therapy were not correlated significantly with alexithymia, psychopathology, or quality of life dimensions. Multiple regression analysis showed that openness and depression were the two predictors for alexithymia in SLE patients. The present findings showed that alexithymia may play an important role in SLE patients. The difficulty in the management of emotions may lead to psychological distress and instability affecting the patient's quality of life, a relevant finding for the psychological, psychiatric, and clinical intervention and approach.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".