Pain Symptomatology and Management in Pediatric Ehlers–Danlos Syndrome: A Review
Bibliographic record
Abstract
Ehlers-Danlos syndromes (EDS) are a group of connective tissue disorders that manifest with hyperextensibility of joints and skin, and general tissue fragility. While not a major criterion for clinical diagnosis, pain is a frequently endorsed symptom across subtypes of EDS. As such, the present review aims to summarize research to date on pain characteristics and management, and the relationship between such pain symptomatology and quality of life in pediatric EDS. Characteristics of pain, including theorized etiology, relative intensity and extent of pain are described, as well as descriptions of frequently endorsed pain sites (musculoskeletal, and non-musculoskeletal). Interventions related to the management of musculoskeletal (e.g., pharmaceutical intervention, physical therapy) and non-musculoskeletal pain (e.g., pharmaceutical and psychological interventions) are discussed, highlighting the need for additional research related to pediatric pain management in the context of hypermobility syndromes. In addition, the relationship between pain in pediatric EDS and quality of life is described. Finally, limitations of literature to date are described and recommendations for future lines of research are outlined.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".