Unloading shoes for osteoarthritis of the knee: protocol for the SHARK randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (OA) is a common and disabling condition. Abnormalities in knee loading play an important role in disease pathogenesis, yet there are few non-surgical treatments for knee OA capable of reducing knee load. This two-arm randomised controlled trial is investigating the efficacy of specially-designed unloading shoes for the treatment of symptoms in people with knee OA. METHODS/DESIGN: 164 people with symptomatic medial tibiofemoral joint OA will be recruited from the community and randomly allocated to receive either unloading shoes or control shoes. Unloading shoes have a specially-designed triple-density midsole where the medial side is softer than normal and the lateral side harder as well as a lateral wedge between the sole and sock-liner. Control shoes are standard athletic shoes and do not contain these features. Participants will be blinded to shoe allocation and will be instructed to wear the shoes as much as possible every day for 6 months, for a minimum of 4 hours per day. The primary outcomes are knee pain (numerical rating scale) and self-reported physical function (Western Ontario and McMaster Universities Osteoarthritis Index) measured at baseline and 6 months. Secondary outcomes include additional measures of knee pain, knee stiffness, participant global ratings of change in symptoms, quality-of-life and physical activity. CONCLUSIONS: The findings from this study will help determine whether specially-designed unloading shoes are efficacious in the management of knee OA. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry reference: ACTRN12613000851763.
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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 | 0.031 | 0.033 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.120 | 0.018 |
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".