The GoodHope Exercise and Rehabilitation (GEAR) Program for People With Ehlers-Danlos Syndromes and Generalized Hypermobility Spectrum Disorders
Bibliographic record
Abstract
Introduction: The Ehlers-Danlos Syndromes (EDS) and Generalized Hypermobility Spectrum Disorders (G-HSD) comprise a heterogeneous group of genetic disorders of abnormal synthesis and/or maturation of collagen and other matricellular proteins. EDS is commonly characterized by manifestations such as multi joint hypermobility that can lead to musculoskeletal pains, subluxations and dislocations, fragile skin, organ dysfunction, and chronic significant diffuse pain with fatigue, deconditioning eventuating to poor quality of life. Evidence suggests exercise and rehabilitation interventions may ameliorate symptoms of unstable joints, recurrent subluxations/dislocations, and chronic widespread musculoskeletal pain. To date, there have only been a few reports describing exercise and rehabilitation care strategies for people with EDS. Methods: In this manuscript, we describe the GoodHope Exercise and Rehabilitation (GEAR) program, its overarching principles, as well as the program development and delivery model. The GEAR program aims to decrease functional impairment, reduce pain, increase confidence in symptom self-management, and provide a community of support for people with EDS/G-HSD. To achieve these goals, we detail the model of care that includes exercise and rehabilitation therapy, education for self-management, and support accessing relevant community resources. Strengths and Limitations of the Study: GEAR represents a novel exercise and rehabilitation care model for people with G-HSD and various clinical EDS subtypes, beyond the commonly included hEDS subtype. Systematic collection of data via validated measurements is ongoing and will guide the refinement of GEAR and support the development of emerging exercise and rehabilitation programs for people with EDS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".