Psychological profiles in patients with Sjogren's syndrome related to fatigue: a cluster analysis
Bibliographic record
Abstract
OBJECTIVE: Fatigue is a highly prevalent and debilitating symptom in the autoimmune disease SS. Although the disease process plays a role in fatigue, psychological factors may influence fatigue and the ability to deal with its consequences. Profiles of co-occurring psychological factors may suggest potential targets for the treatment of fatigue. The aim of this study was to identify psychological profiles in patients with SS and the accompanying levels of fatigue. METHODS: Three hundred patients with primary SS (mean age 57 years, 93% female) completed questionnaires on fatigue (multidimensional fatigue inventory), physical activity cognitions (TAMPA-SK), illness cognitions, cognitive regulation, emotion processing and regulation [Toronto Alexithymia Scale 20, Emotion Regulation Questionnaire (ERQ), Berkeley Expressivity Questionnaire], coping strategies (Brief COPE) and social support. RESULTS: Principal axis factor analysis (oblimin rotation) yielded six psychological factors: social support, negative thinking, positive thinking, emotional expressivity, avoidance and alexithymia (i.e. the inability to differentiate emotions). Using cluster analyses, these factors were grouped in four psychological profiles: functional (39%), alexithymic (27%), self-reliant (23%) and dysfunctional (11%). Irrespective of the psychological profile, the level of fatigue was substantially higher in patients than in the general population. Patients with a dysfunctional or an alexithymic profile reported more fatigue than those with a self-reliant profile. CONCLUSION: Our study in SS yielded four psychological profiles that were differentially associated with fatigue. These profiles can be used to examine determinants and prognosis of fatigue as well as the possibility of customizing cognitive behavioural interventions for chronic fatigue.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".