Patient engagement in the design of an intervention to prevent muscle loss in individuals with knee osteoarthritis and a body mass index (BMI) ≥ 35
Bibliographic record
Abstract
OBJECTIVE: ) often prioritise weight-loss, which may overshadow specific benefits for physical function, metabolic health, and body composition. As part of the development of a future clinical study, we gathered perspectives from individuals living with knee OA and a large body size to inform the proposed intervention design and delivery. METHODS: . An anonymous electronic survey was distributed on social media between April 2020 and June 2020. Open-ended questions addressed a proposed 12-week multimodal intervention (involving targeted nutrition, resistance exercises, and self-management support). An optional semi-structured interview was offered upon completion, with interviews recorded and transcribed verbatim. Reflexive thematic analysis and interpretation guided by an acceptability framework was used to identify recommendations for the intervention design and delivery. RESULTS: Twenty individuals living across Canada completed the survey (100% female; 18 aged <65 years and 2 ≥ 65 years). Ten individuals completed the interview. From aggregate survey and interview data, three recommendations were generated: (1) the effectiveness of the intervention for health improvement (specifically mobility and pain) must be emphasised to avoid perceived weight-loss expectations; (2) extend support beyond 12-weeks and consider terminology free from weight-bias to enhance acceptance; (3) include optional customisation of intervention delivery to reduce acceptability-related burden. CONCLUSION: These female patient-derived recommendations may improve perceived intervention acceptability, and thereby may enhance participant enrolment and retention in clinical trials.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".