Successful Management of Waja’al-Rukba (Knee Osteoarthritis) with Unani Pharmacopeial Poultice: A Case Study
Bibliographic record
Abstract
Knee osteoarthritis (Knee OA) is the most common Musculoskeletal disorder, which causes functional disability thus, increasing the economic burden in society. In Unani medicine knee Osteoarthritis is treated with various regimens and drugs. Among these regimens, Dimād (Poultice) is prepared with several single Unani pharmacopeial drugs that exert anti-inflammatory, analgesic and anti-arthritic actions. Keeping this in mind a case study was conducted to assess the efficacy of Dimād-i-jalinus a pharmacopeial Unani formulation in the management of Knee OA. A 63-year-old female patient with chief complaints of pain and swelling in the left knee joint, joint stiffness and mild tenderness feeling severe pain while performing daily activity for the last 6 months was treated at OPD of Hakim Syed Ziaul Hasan Govt. (Auto) Unani Medical College & Hospital, Bhopal. Dimād-i-jalinus was given to the patient, in paste form and the patient was explained to use this paste over the anterior surface of the Left knee joint and the bandage was wrapped to keep the medicines in place overnight every day for 14 days. The patient was assessed at baseline, 7th day and 14th day based on changes in subjective and objective parameters with the help of Visual Analogue Scale (VAS) and Western Ontario and McMaster Universities Arthritis Index (WOMAC). The present study reveals that Dimād-i-jalinus is safe and effective in treating Knee OA without any side effects. Therefore, further randomized clinical studies need to be carried out to validate the effectiveness of Dimād-i-jalinus in the management of Knee OA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".