Effectiveness of Habbe Gule Aakh in Osteoarthritis Knee: A Randomized Clinical Trial
Bibliographic record
Abstract
Background: Osteoarthritis (OA) of the knee is a degenerative disorder leading to joint pain and functional limitations. Traditional Unani medicine has employed herbal remedies, such as Habbe Gule Aakh (HGA), to manage musculoskeletal conditions. However, empirical evidence supporting its efficacy is limited. Objective: This study aimed to evaluate the effectiveness of HGA in different doses for osteoarthritis management using clinical and radiological parameters. Methods: A randomized, single-blind, parallel-arm comparative study was conducted at the National Institute of Unani Medicine, Bengaluru. Sixty participants diagnosed with knee OA were categorized into four groups based on Kellgren-Lawrence grading. Group I received 500 mg/day, Group II 1000 mg/day, Group III 1500 mg/day, and Group IV 2000 mg/day of HGA. The assessment was conducted at baseline, and on the 7th, 14th, and 21st days using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Visual Analogue Scale (VAS). Statistical analysis included ANOVA and paired t-tests. Results: Significant improvement (p<0.001) was observed in all groups, with higher doses showing greater efficacy in reducing pain and improving joint function. No adverse effects were reported. Conclusions: HGA demonstrated promising results in knee OA management. Further large-scale studies with extended follow-ups are warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".