The Impact of Trauma and Emotional Regulation in Fibromyalgia, Chronic Migraine, and Their Comorbidity: A Comparative study among 3 hospitals in Italy.
Bibliographic record
Abstract
Background: Research highlights a high prevalence of early traumatic experiences (psychological, physical and sexual) connected to defense mechanism and alexithymia (difficulty in identifying and expressing emotions) in nociplastic pain. However, little is known about their specificities in different diagnosis such as fibromyalgia (FM), chronic migraine (CM), and their comorbid appearance (Fibromig). Objectives: This study aimed to compare traumatic experiences, defense mechanisms, and alexithymia traits among women diagnosed with FM, CM, and Fibromig. We hypothesized that the Fibromig group would report higher levels of trauma, maladaptive defenses, and alexithymia compared to FM and CM groups. Methods: Data were collected from 295 women across three hospitals in Rome, Milan and Pavia, Italy. Validated tools were used to assess trauma and emotional regulation, including the Traumatic Experience Checklist, the Defense Mechanism Rating Scale, and the Toronto Alexithymia Scale-20. Results: The Kruskal-Wallis test revealed significant differences across several measures: compared to CM, the Fibromig and FM groups reported more traumatic experiences, particularly emotional neglect (p<.003), with the Fibromig group also showing a higher impact of sexual harassment (p<.026). No group differences were observed for emotional, physical, or sexual abuse, nor for the use of immature, neurotic, or mature defenses to cope with stress. Alexithymia was significantly higher in Fibromig and FM groups compared to CM (p<.001). Conclusion: This study highlights emotional neglect and sexual harassment as key areas of trauma in FM and Fibromig patients. Additionally, the findings underscore the importance of addressing alexithymia in these groups to improve emotional well-being and overall patient care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".