CLINICAL EFFICACY AND SAFETY OF LEECHING IN KNEE OSTEOARTHRITIS
Bibliographic record
Abstract
Background: Leeching Therapy (Irsal-e-Alaq) is an integral part of regimental therapy used in Unani System of Medicine. It has been successfully practiced by Greeko-Arabic physicians in the management of Joint disorders since antiquity. Osteoarthritis is the commonest form of arthritis and is a major cause of pain and disability in the elderly. The incidence of arthritis has increased considerably in present scenario. Objective: The present study was aimed to evaluate the efficacy and safety of Leeching Therapy (Irsal-e-Alaq) in the patients of knee Osteoarthritis on scientific parameters. Methodology: The study was conducted in the Regimental Therapy ward RRIUM, Srinagar. 75 patients of OA were enrolled in the trial after obtaining their informed consents. All the patients were clinically assessed and diagnosed on the basis of thorough history, clinical and radiological examination of the affected joint. Then 2 fresh leeches were applied on the affected areas of the beard till leeches were belly filled and fell down of their own (approximately 45 minutes). Then antiseptic dressing was applied on the site. This leech therapy was done every after 15 days for two months. The efficacy of Leeching therapy was assessed on the basis of Western Ontario and McMaster University (WOMAC) Score and Visual Analogue Scale (VAS) Score. Results: The results were analyzed statistically by using a Paired Student’s t-test. There was a significant reduction in the WOMAC and VAS scores of post-treatment group (P <0.01) as compared to pre-treatment group. Conclusion: It is concluded in this observational non comparison study that Leeching therapy may be used safely and effectively in the management of Osteoarthritis.
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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 | 0.000 | 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".