Stressful Life Events and Psychosomatic Symptoms in Fibromyalgia Syndrome and Rheumatoid Arthritis
Bibliographic record
Abstract
Objective: The study analyzed the role of traumatic experiences and psychosomatic components as potential predictors of the likelihood of chronic pain patients having or not having fibromyalgia. Methods: We examined the role of stressful life events (Traumatic Experiences Checklist), psychosomatic syndromes (Toronto Alexithymia Scale and Diagnostic Criteria for Psychosomatic Research), pain, and psychological distress (Beck Depression Inventory—II and State-Trait Anxiety Inventory) in 104 patients with fibromyalgia compared with a sample of 104 patients with rheumatoid arthritis. Results: Patients with fibromyalgia reported significantly more traumatic events, a higher prevalence of psychosomatic syndromes, and higher levels of pain, anxiety and depressive symptoms compared with patients with rheumatoid arthritis (all p < 0.01). Hierarchical binary logistic regression with group membership as the dependent variable showed that somatization syndromes (OR = 3.67), pain (OR = 1.56), and childhood trauma (OR = 1.11) were statistically significant predictors of group belonging, and the model explained 67% of the variance in diagnosis [χ2(9) = 143.66, p < 0.001]. Conclusion: These results highlighted that patients with fibromyalgia are characterized primarily by marked somatization and a high prevalence of early stressful life events compared with patients with rheumatoid arthritis, a primarily nociceptive chronic pain condition. A better knowledge of these mechanisms could allow clinicians to develop tailored interventions that take greater account of the psychological dimension of the disease.
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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 | 0.001 | 0.003 |
| 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".