A feasibility randomised trial evaluating the levitation tri-compartment offloader knee brace for multicompartment knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: The Levitation™ "Tri-Compartment Offloader" (TCO) knee brace (Spring Loaded Technology) is designed to reduce pain for individuals with knee osteoarthritis (OA). The TCO is available on the market, however, has not been compared to the current standard of care treatment for knee OA with a controlled clinical trial. This feasibility study aimed to (i) evaluate the feasibility of conducting a full RCT, (ii) evaluate the distributional properties of the Visual Analog Scale (VAS) activity-specific knee pain score to estimate the sample size required for a full randomised controlled trial (RCT), and (iii) refine and optimise the study protocol. METHODS: A prospective, 3-group, parallel, single-centre feasibility RCT of individuals with moderate to severe patellofemoral or multicompartment knee OA was undertaken at the University of Calgary (Alberta, Canada). Participants were randomised using a 1:1:1 random allocation to one of three intervention groups: standard of care (Control), Control plus a knee sleeve (Sleeve), or Control plus a TCO brace (TCO). Participants were assessed at baseline (before intervention) and after 6 weeks and 3 months (primary endpoint) of controlled intervention. The sample size for a full RCT was estimated based on the change in VAS knee pain between baseline and 3 months. Feasibility was assessed using participant recruitment, intervention adherence, participant response rates, data quality, dropout rate and adverse events. All protocol changes made throughout the duration of the study were recorded. RESULTS: Twenty-nine participants (13 females; age: 62 ± 9 years) were recruited. The estimated sample size for a full RCT is 93 individuals (31 per group). Participants showed high intervention adherence and follow-up rates were 86% at 3 months. Four participants dropped out of the study, and there were 3 adverse events reported. Changes were made to participant eligibility criteria, recruitment strategy and data collection methods to improve feasibility, efficiency, and appropriateness for a full RCT. CONCLUSIONS: This study supports the feasibility of a full scale RCT evaluating the clinical effectiveness of the TCO knee brace compared to the current (conservative) standard of care treatment for individuals with knee OA, and an adequately powered RCT is now warranted. TRIAL REGISTRATION: ClinicalTrials.gov, ID: NCT05543486. Registered 15 September 2022-retrospectively registered, https://clinicaltrials.gov/study/NCT05543486.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".