Evaluating the efficacy of Internet-Based Exercise programme Aimed at Treating knee Osteoarthritis (iBEAT-OA) in the community: a study protocol for a randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Knee osteoarthritis (OA) is the most common joint disease worldwide. As of today, there are no disease-modifying drugs, but there is evidence that muscle strengthening exercises can substantially reduce pain and improve function in this disorder, and one very well tested physiotherapy protocol is the 'Better Management of Patients with Osteoarthritis' developed in Sweden. Given the high prevalence of knee OA, a potentially cost-effective, digitally delivered approach to treat knee OA should be trialled. This study aims to explore the benefits of iBEAT-OA (Internet-Based Exercise programme Aimed at Treating knee Osteoarthritis) in modulating pain, function and other health-related outcomes in individuals with knee OA. METHODS AND ANALYSIS: A randomised controlled trial was designed to evaluate the efficacy of a web-based exercise programme in a population with knee OA compared with standard community care provided by general practitioners (GPs) in the UK. We anticipate recruiting participants into equal groups. The intervention group (n=67) will exercise for 20-30 min daily for six consecutive weeks, whereas the control group (n=67) will follow GP-recommended routine care. The participants will be assessed using a Numerical Rating Scale, the Western Ontario and McMaster Universities Osteoarthritis Index, the Arthritis Research UK Musculoskeletal Health Questionnaire, the Pittsburgh Sleep Quality Index, 30 s sit to stand test, timed up and go test, quantitative sensory testing, musculoskeletal ultrasound scan, muscle thickness assessment of the vastus lateralis, and quadriceps muscles force generation during an isokinetic maximum voluntary contraction (MVC). Samples of urine, blood, faeces and synovial fluid will be collected to establish biomarkers associated with changes in pain and sleep patterns in individuals affected with knee OA. Standard parametric regression methods will be used for statistical analysis. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Research Ethics Committee (ref: 18/EM/0154) and the Health Research Authority (protocol no: 18021). The study was registered in June 2018. The results of the trial will be submitted for publication in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: NCT03545048.
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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 | 0.067 | 0.049 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.009 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.060 | 0.011 |
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".