The Effectiveness of Conservative Interventions on Pain, Function, and Quality of Life in Adults with Hypermobile Ehlers-Danlos Syndrome/Hypermobility Spectrum Disorders and Shoulder Symptoms: A Systematic Review
Bibliographic record
Abstract
Objective: To synthesize the evidence on conservative interventions for shoulder symptoms in hypermobile Ehlers-Danlos Syndrome (hEDS) and hypermobility spectrum disorder (HSD). Data Sources: A literature search was conducted using data sources Medline, PEDro, CINAHL, AMED, Elsevier Scopus, and the Cochrane Library from January 1998 to June 2023. Study Selection: The review included primary empirical research on adults diagnosed with hEDS or HSD who experienced pain and/or mechanical shoulder symptoms and underwent conservative interventions. Initially, 17,565 studies were identified, which decreased to 9668 after duplicate removal. After title and abstract screening by 2 independent authors, 9630 studies were excluded. The full texts of the remaining 38 were assessed and 34 were excluded, leaving 4 articles for examination. Data Extraction: Two authors independently extracted data using a predefined extraction table. Quality assessment used the Joanna Briggs Institute checklists and the Template for Intervention Description and Replication. Data Synthesis: The review covered 4 studies with a total of 7 conservative interventions, including exercise programs, kinesiology taping, and elasticized compression orthoses. Standardized mean differences were calculated to determine intervention effects over time. The duration of interventions ranged from 48 hours to 24 weeks, showing positive effect sizes over time in the Western Ontario Shoulder Instability Index, pain levels, improved function in activities of daily living, and isometric and isokinetic strength. Small to negligible effect sizes were found for kinesiophobia during completion of exercise programs. Conclusions: Shoulder symptoms in hEDS/HSD are common, yet significant gaps in knowledge remain regarding conservative interventions, preventing optimal evidence-based application for clinicians. Further research is necessary to explore the most effective intervention types, frequencies, dosages, and delivery methods tailored to the specific requirements of this patient population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".