The Relations among the Fear-Avoidance Model of Chronic Pain, Fear of Falling, and Disability in hypermobile Ehlers-Danlos Syndrome and Hypermobility Spectrum Disorder
Bibliographic record
Abstract
Hypermobile Ehlers-Danlos Syndrome (hEDS) and Hypermobility Spectrum Disorder (HSD) are two understudied chronic pain conditions characterized by connective tissue dysfunction with hallmark hypermobility. These conditions include disease manifestations of chronic pain, frequent dislocations, reduced muscle strength, and proprioceptive difficulties, which singularly or in combination, lead to disability. The current study examined the impact of fear-avoidance [FA] constructs (as per the FA Model of Pain, including pain catastrophizing, pain hypervigilance, pain-related fear, depression, anxiety) and fear of falling on disability in hEDS/ HSD. Group differences between hEDS/HSD participants and healthy controls were assessed. A total of 168 individuals with hEDS/HSD and 108 controls participated. Pain catastrophizing, anxiety, and fear of falling predicted 26.5% of the variance in disability, over and above pain severity and age. Participants with hEDS/HSD showed significantly higher scores on all constructs, with exception of anxiety and depression, which did not differentiate the groups. This novel work in hEDS/HSD mirrors FA studies done in other chronic pain conditions and provides evidence of the relationship between FA factors, pain experience, and disability in this patient population. Future research is needed to apply these findings to create individualized interventions that improve disability, and therefore quality of life, for the hEDS/HSD population.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".