How Do Patients with Hypermobile Ehlers-Danlos Syndrome Cope with This Medical Condition? An Analysis of Autobiographical Narratives in Relation to Pain Perception and Affect Regulation Capabilities
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Hypermobile Ehlers-Danlos syndrome (hEDS) is the most common form of EDS, characterized by joint hypermobility, skin findings, and joint pains or recurrent dislocations that may also be associated with other several extra-articular symptoms. A deficit in the affect regulation represents a risk element in the development of both physical and mental health, as well as in a greater pain perception. The present study aims at exploring the associations between linguistic characteristics associated with different autobiographical memories and affect regulation and pain measures in patients affected by hEDS. A further aim is to explore the possible differences in linguistic measures between different episodes. METHODS: Twenty-five patients with hEDS diagnoses (mean age = 38.32; SD = 17.00; 23 female) in treatment at the Physical Medicine and Rehabilitation Department of Umberto I Hospital in Rome completed a socio-demographic questionnaire, the Difficulties in Emotion Regulation Scale (DERS), the 20-item Toronto Alexithymia Scale (TAS-20), and the Brief Pain Inventory (BPI), as well as an interview aimed at collecting memories regarding neutral, positive, and negative events and the medical condition. The transcriptions of the interviews were analyzed using a computerized linguistic measure of the referential process (RP). RESULTS: A correlational analysis showed several significant associations among the linguistic measures, affect regulation, and perception of pain, applied to neutral, positive, and disease condition narratives. Only few significant associations emerged regarding the negative episode. Moreover, significant differences emerged between the neutral event compared with the positive, negative, and diagnosis episodes, especially with the latter. CONCLUSIONS: The present findings seem to confirm the association between affect regulation, pain, and linguistic measures, sustaining an elaborative process. Specifically, the experience of chronic pain associated with the discovery of the rare disease becomes a meaningful experience in one's life condition and supports the ability to cope with the experience of chronicity.
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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 | 0.004 |
| 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 | 0.001 |
| 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".