Ethnomedicinal Evaluation of Karmaranga (Averrhoa Carambola Linn.) Fruit Oil in Sandhigatavata w.s.r. to Osteoarthritis of Knee: An Exploratory Clinical Study
Bibliographic record
Abstract
ABSTRACT Background: India’s rich biodiversity supports extensive ethnomedicine, where plants like Averrhoa carambola Linn. ( Karmaranga ) are used by folklore healers to treat conditions such as rheumatism and joint pain. In Ayurveda, joint pain ( Sandhi shula ) is linked to Sandhigata Vata , comparable to osteoarthritis (OA), a condition with high prevalence and significant impact on quality of life and healthcare costs. Aim: The aim of the study was to assess the therapeutic potential of fruit oil from Averrhoa carambola Linn. in the management of Janu Sandhigata vata (~OA of the Knee). Materials and Methods: Oil from Averrhoa carambola Linn. fruits was prepared and analyzed. A clinical study evaluated its efficacy in managing Sandhigata Vata concerning knee OA. Thirty patients were randomly selected and treated with Karmaranga Taila ( Madhyama Paka ) internally (15 drops twice daily) and Karmaranga Taila ( Khara Paka ) for local application once at night. Clinical changes were assessed every 14 days up to 28 days, with a follow-up 15 days posttreatment using various parameters such as joint pain, stiffness, swelling, crepitus, and tenderness and standard scales such as Oswestry Disability Index and Western Ontario and McMaster Universities Arthritis Index (WOMAC). Results: The clinical study revealed that Averrhoa carambola Linn. fruit oil significantly reduced pain, stiffness, crepitus, swelling, tenderness, restricted movements, and time taken to walk 30 m, climb 10 steps, and do 10 sit-ups, along with WOMAC and Oswestry disability scores. There was no significant change in radiological findings or patellar tap test results. Conclusion: Averrhoa carambola Linn. fruit oil, used both internally and externally, is beneficial in managing knee OA and warrants further exploration in larger samples.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".