Effect of Mud-Bath Therapy on Serum Biomarkers in Patients with Knee Osteoarthritis: Results from a Randomized Controlled Trial.
Bibliographic record
Abstract
BACKGROUND: Balneotherapy is one of the most commonly used non-pharmacological approaches for osteoarthritis (OA). Recent data indicate that some biomarkers could be useful to predict OA progression and to assess therapeutic response. OBJECTIVES: To evaluate the effects of mud-bath therapy on serum biomarkers in patients with knee OA. METHODS: The study group comprised 103 patients with primary symptomatic bilateral knee OA who were randomly assigned to receive a cycle of mud-bath therapy over a period of 2 weeks or to continue their standard therapy alone. Clinical and biochemical parameters were assessed at baseline and after 2 weeks. Clinical assessments included global pain score on a visual analogue scale (VAS) and the Western Ontario and McMaster Universities Index (WOMAC) subscores for knee OA. Cartilage oligomeric matrix protein (COMP), C-terminal cross-linked telopeptide type II collagen (CTX-II), myeloperoxidase (MPO) and high sensitivity C-reactive protein (hsCRP) serum levels were assessed by ELISA. RESULTS: At the end of mud-bath therapy we observed a statistically significant improvement in VAS and WOMAC subscores. Serum levels of COMP, MPO and hsCRP did not show any significant modification in either group, while a significant increase (P < 0.001) in CTX-II serum levels was observed in the mud-bath group after the treatment. CONCLUSIONS: A cycle of mud-bath therapy added to the usual treatment had a beneficial effect on pain and function in patients with knee OA. The evaluation of serum biomarkers showed a significant increase of CTX-II only, perhaps due to an increase of cartilage turnover induced by thermal stress.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".