Evaluation of Yograj Guggulu, Ashwagandha Churna and Narayana Taila in management of Osteoarthritis Knee: A study in tribal dominant community
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis (OA) is the most prevalent joint disease and a major cause of joint impairment and physical debility, common in elderly, women and laborious workers. The available non-steroidal anti-inflammatory drugs (NSAID) are being prescribed to manage the condition; however, newly discovered alternatives are looked upon by the practitioners. Thus, the study was aimed to provide an effective and safer alternative through Ayurveda for the management of OA. OBJECTIVE: To document the role of the selected Ayurveda formulations in the management of OA and to assess the tolerability of the formulations. MATERIAL AND METHODS: It was an open-label, multicentric, single-arm, prospective, study conducted at 14 peripheral institutes of the Central Council for Research in Ayurvedic Sciences, New Delhi. 483 participants of any gender between the age 40 to 65 years diagnosed with OA knee as per the ACR diagnostic criteria (2012) and willing to provide consent were enrolled in the study. Oral administration of Ayurvedic formulations Yograj Guggulu, Ashwagandha Churna and the local application of Narayana Taila was given for 12 weeks and assessment was done by means of Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Modified-CRD, Pune version Score, Visual Analogue Scale score and disease-specific symptoms on 28th, 56th, 84th and 112th day. RESULTS: Significant change (P<0.001) was observed in WOMAC score, VAS score and cardinal symptoms of OA knee. No adverse events reported in the study and the study drugs were well tolerated by the participants. CONCLUSION: The study substantiates that administration of Yograj guggulu, Ashwagandha Churna and Narayana Taila, is well acceptable and tolerable. The interventions effectively alleviate the cardinal symptoms of OA knee.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".