Comparative efficacy of different weight loss treatments on knee osteoarthritis: A network meta‐analysis
Bibliographic record
Abstract
The lifetime risk of developing symptomatic knee osteoarthritis is 60% in subjects with obesity. It is unclear which is the best weight loss interventions leading to a meaningful improvement of osteoarthritis symptoms and clinical conditions in subjects with obesity. Our network meta-analysis compares different weight loss interventions on the improvement of osteoarthritis symptoms and clinical conditions in subjects affected by obesity. PubMed, Embase, and Cochrane databases were systematically searched for eligible studies until November 2020. Thirty eligible studies comprising 4651 adults (74.6% women) were included. The most effective interventions reducing pain were bariatric surgery, low-calorie diet and exercise, and intensive weight loss and exercise (-62.7 [95% CrI: -74.6, -50.6]; -34.4 [95% CrI: -48.1, -19.5]; -27.1 [95% CrI: -40.4, -13.6] respectively). For every 1% weight loss Western Ontario and McMaster Universities Osteoarthritis (WOMAC) pain, function, and stiffness scores decreased by about 2% points. In conclusion, our meta-analysis shows that a substantial weight loss is necessary to reduce significantly knee pain and joint stiffness and to improve physical function: 25% weight reduction from baseline is necessary to obtain a 50% reduction of each subscale of the WOMAC score. However, performing physical exercise is essential to preserve the lean body mass and to avoid sarcopenia. Our results apply to a large spectrum of body mass index (BMI), from overweight to severe obesity.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.046 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".