The Efficacy of a Topical Gel Prepared Using Lake Urmia Mud in Patients with Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: Osteoarthritis (OA) is characterized by progressive cartilage degradation and secondary inflammation of the synovial membrane. Studies have shown that mud therapy may influence serum levels of several cytokines involved in the pathogenesis of OA and chondrocyte metabolism. The main targets of our research were to make use of Lake Urmia mud for topical gel formulation, evaluation of the possible therapeutic effects of this gel on symptoms of patients with knee OA and the evaluation of serum tumor necrosis factor-alpha (TNF-alpha) level after mud therapy in comparison with the application of a placebo. METHODS: Fifty (50) patients suffering from knee OA participated in this study and randomized into two groups: case group and control group. Patients in the case group received mud therapy and the placebo was applied to patients in the control group. Three (3) parameters including pain, morning stiffness, and joint functionality were assessed in all patients. Visual analogue scale and Western Ontario McMaster Osteoarthritis index (WOMAC) were the employed scales for pain assessment. Functional capacity was evaluated by using WOMAC functional capacity and WOMAC global index. All the mentioned steps were done before and after treatment. Blood samples, in both groups, were collected for measuring tumor necrosis factor (TNF)-alpha serum level. RESULTS: All the differences (for three parameters), in the case group, were statistically significant. TNF-alpha serum level reduction in both groups were detected: 19.41% in the case group and 1.76% in the control group. CONCLUSIONS: Mud therapy using formulated gel is an effective method in knee OA treatment and pain reduction. Further studies may be needed for the evaluation of possible synergism between pharmacological treatment and mud therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".