Protocol for the POMELO (Prevention Of MusclE Loss in Osteoarthritis) randomized pilot feasibility trial
Bibliographic record
Abstract
Objective: ] have a higher risk of complications with total knee arthroplasty (TKA), and hence may be ineligible for surgery unless they reduce their BMI. However, pre-TKA weight-loss has not been shown to reduce surgical infection risk and may inadvertently increase risk for muscle loss and development of sarcopenic obesity (low muscle and low strength with higher fat mass). This suggests that a knee OA management approach that doesn't focus on weight change (weight-neutral) may be beneficial. This study examines if a weight-neutral behavioural intervention is feasible and acceptable to participants, and improves muscle mass and physical function in comparison to usual care. Design: This pilot randomized clinical trial compares a 12-week multimodal intervention [including targeted nutrition, progressive resistance exercise, and arthritis self-management support] to usual care. Co-primary outcomes are feasibility and acceptability, with secondary outcomes of change in lean soft tissue and physical function within and between groups at 3-months and 9-months from baseline. Change in waist circumference, fat mass, blood biomarkers, energy metabolism, OA-related pain and function, health-related quality of life, self-efficacy for arthritis management, and interest in pursuing a TKA within and between groups will be explored. Conclusion: This study will inform future development of more personalized knee OA treatment approaches for adults with larger bodies. Further, this will contribute to effective alternative treatment pathways that reduce inequities in access to OA care for this understudied patient population.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.117 | 0.021 |
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".