Possible rheumatoid arthritis subtypes in terms of rheumatoid factor, depression, diagnostic delay and emotional expression: an exploratory case-control study
Bibliographic record
Abstract
INTRODUCTION: Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis has been implicated in the pathology of rheumatoid arthritis (RA), particularly as vulnerable personality types are exposed to chronic stress. Emotions are powerful modulators of stress responses. However, little is known about whether patients with RA process emotions differently to matched controls. In this study we: 1) assessed whether the trait emotional intelligence (trait EI) scores of patients with RA differ from healthy controls at the facet level; 2) explored any subgroups in RA, in terms of trait EI and common risk factors. METHODS: A total of 637 patients with RA were compared to 496 controls on the trait EI Questionnaire (TEIQue). RA subgroups were explored in terms of trait EI, rheumatoid factor status (RF+/-), depression and time from onset of symptoms until diagnosis (diagnostic delay). RESULTS: The RA group rated themselves lower on Adaptability, Stress-management, Emotion management, Self-esteem, Sociability, Assertiveness, Impulsiveness and Well-being, and higher on Empathy and Relationships than healthy controls. The RF- subtype reported more time with depression (25.2 vs. 11.3 months), a longer diagnostic delay (3.0 vs. 1.7 years), and greater emotional expression (5.15 vs. 4.72), than the RF+ subtype. These differences were significant at the P <0.05 level, but not following strict Bonferroni corrections and should therefore be treated as indicative only. RF- patients with a longer diagnostic delay reported depression lasting three times longer (42.7 months), when compared to three other subtypes (11.0 to 12.7 months). CONCLUSIONS: RA patients and controls differ in their emotion-related personality traits, as operationalized by trait EI. These differences may make people with RA more susceptible to chronic stress and HPA-axis dysregulation. RA may be a highly heterogeneous illness where at least two subtypes may be characterized by personality, psychiatric and immunological differences. RF- status, as well as diagnostic delay and emotional expression, may predict future risk of depression. Research on the causes of RA could benefit from a systems science 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".