Efficacy of Balneotherapy and Mud Therapy in Patients with Knee Osteoarthritis: A Systematic Literature Review
Bibliographic record
Abstract
Abstract Objectives To identify literature reporting on thermal mineral water and mud therapy effectiveness on pain, stiffness and knee function in patients with knee osteoarthritis. Design Systematic evidence scan of MEDLINE and PubMed was performed to identify the randomized controlled trial studies published from 2004 to December 2018. Study selection Papers reporting the effect of balneotherapy and mud therapy for treating knee OA, a duration of ≥2 weeks and in which Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores were used as an outcome measure. Data extraction Not RCT, Studies not in English. Results A quantitative meta-analysis of ten studies (831 patients) was performed. Five clinical studies (407 patients) measured effectiveness of balneotherapy and there was significant difference between the groups in WOMAC pain score, WOMAC stiffness score and WOMAC function score, with the differences in favour of balneotherapy. Six clinical studies (500 patients) measured effectiveness of mud therapy and there was significant difference between the groups in WOMAC pain score, WOMAC stiffness score and WOMAC function score, with the differences in favour of mud therapy. Conclusion This meta-analysis indicates that balneotherapy and mud therapy were clinically effective in relieving pain, stiffness, and improving function, as assessed by WOMAC score.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".