037 Heavy shoulder strengthening exercises in patients with hypermobility and long-lasting shoulder symptoms: a feasibility study
Bibliographic record
Abstract
Background Patients with hypermobility spectrum disorder (HSD) are in great risk of experiencing shoulder symptoms, but evidence for exercise as treatment is sparse. Objective To evaluate the feasibility of heavy shoulder strengthening exercise in patients with HSD and long-lasting shoulder symptoms. Design Feasibility study. Setting Primary care. Patients (or Participants) Twelve patients (39.3±13.9 years) with HSD and shoulder instability and/or pain >3 months. Interventions (or Assessment of Risk Factors) 16-week progressive heavy shoulder strengthening programme 3 times/week using exercises targeting scapular and rotator cuff muscles. Main Outcome Measurements Pre-defined progression criteria included recruitment rate (acceptable: 6 patients/month), test duration (acceptable: <120 min), patient retention (acceptable: >80% complete intervention), training adherence (acceptable: >75% adhere to >36 training sessions), adverse events (acceptable: minor events with no patients discontinuing the study), besides patient and physiotherapist feedback. Treatment outcomes were assessed using patient-reported health parameters, such as the Western Ontario Shoulder Instability Index (WOSI (0–2100, better to worse)), outcomes on pain, fatigue and kinesiophobia, besides isometric shoulder strength and clinical tests (shoulder instability, hypermobility and proprioception). Results Recruitment rate was 5.6/month, assessment duration (mean±SD) 105±9 min, retention 100%, adherence 83%, and four patients experienced short-lasting soreness/pain. Patient feedback was positive, and physiotherapists found the intervention relevant and applicable to the patient-group. WOSI total score improved by 51% (mean±SD, points: baseline 1037±215; follow-up 509±365; mean change (95% CI), 528 (318; 738)), and patients reported reduced pain, fatigue and kinesiophobia. Shoulder strength measurements improved by 28–31% (mean change (95% CI), Nm/kg: scaption 0.51 (0.23; 0.78); internal rotation 1.32 (0.70; 1.95); and external rotation 0.89 (0.37; 1.40)). Clinical tests indicated increased shoulder stiffness. Conclusions The shoulder strengthening programme was feasible in patients with HSD and long-lasting shoulder symptoms. A future randomised controlled trial, with an improved recruitment strategy, will demonstrate whether the exercise programme is effective in improving symptoms in this patient group.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".