Pain Management through Neurocognitive Therapeutic Exercises in Hypermobile Ehlers–Danlos Syndrome Patients with Chronic Low Back Pain
Bibliographic record
Abstract
Background. The hypermobile type of Ehlers–Danlos syndrome (hEDS) is likely the most common hereditary disorder of connective tissue mainly characterized by joint hypermobility. Patients with hEDS suffer joint pain, in particular low back pain, commonly resistant to drug therapy. The aim of this research was to evaluate a neurocognitive rehabilitation approach based not only on the motion and function recovery but also on the pain management. Methods. In this nonrandomized clinical trial, eighteen hEDS patients (4 males and 14 females) with mean age 21 years (range 13‐55) were recruited and evaluated before and after three months of rehabilitation treatment. Results. The outcome scores showed significant statistical results after treatment in reducing pain symptoms (numerical rating scale, P = 0.003; McGill (total score), P = 0.03), fatigue (fatigue severity scale, P = 0.03), fear of movement (Tampa scale, P = 0.003), and pain‐associated disability (Oswestry disability index, P = 0.03). Conclusion. The clinical results observed in our study seem to confirm the role of a specific neurocognitive rehabilitation program in the chronic pain management in the Ehlers–Danlos syndrome; the rehabilitation treatment should be tailored on patient problems and focused not only in the recovery of movement but also on pain perception.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".