Serial Mud Packs Induce Anti-inflammatory Effects in Knee Osteoarthritis – A Randomized, Prospective Clinical Study
Bibliographic record
Abstract
Abstract Background Mud bath and pack have been used to treat musculoskeletal disorders since ancient times. However, the actual mechanisms of action of mud therapy on the inflammatory processes are complex and still not clarified. Methods Therefore, the clinical effects of serial mud packs in patients with knee osteoarthritis were investigated on the molecular level. A total of 52 patients were recruited from an in-patient rheumatology clinic. The participants were randomized in 2 groups: the intervention group (IG, n=26) underwent 9 mud packs in 21 days and a standardized multimodal physical therapy in an in-patient setting, whereas the control group (CG, n=26) only received the multimodal physical therapy. Primary outcome parameters were changes in the serum levels of interleukin(IL)-1ß and IL-10. Secondary outcome parameters were changes of the C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), Western Ontario and Mc Master Universities Arthritis (WOMAC) index and pain (visual analog scale - VAS). Results The IG presented after the serial mud packs significantly decreased pro-inflammatory IL-1ß levels and significantly increased anti-inflammatory IL-10 levels, whereas the CG showed no changes of the 2 cytokines. CRP and ESR remained within in the normal range in both groups without significant changes. Furthermore, the IG presented a significant decrease of the WOMAC index and pain (VAS). Conclusions The results suggest an additive anti-inflammatory effect of serial mud packs within a multimodal physical therapy concept in patients with knee osteoarthritis and could explain the beneficial clinical effects.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".