To study the efficacy of Rhus tox in management of cases of osteoarthritis of knee joint
Bibliographic record
Abstract
Background: Arthritis and rheumatism are among the commonest forms of chronic disease and, with an aging population, are set to become commoner still. Strictly speaking, arthritis means disease of the joints, while rheumatism is disease of the soft connective tissues which support and move the joints. There are many problems with current conventional treatment of arthritis and rheumatism. Although the new generation of NSAIDs is safer, they are only glorified painkillers, which do not affect the basic disease process. Similarly for rheumatoid arthritis, a range of powerful drugs is available but all of these have long and alarming lists of side effects. Thus the main aim of this research study is to prove the efficacy of Homoeopathy in managing cases of Osteoarthritis without any side-effects. Methods: 30 cases were selected fitting the case definition. Age group of 45-79 years was chosen for the study. Inclusion and exclusion criteria were laid down. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Kellgren and Lawrence system of classifying the severity of knee-osteoarthritis were used to assess the severity and outcome. Results: All the results were tabulated. Graphical presentation was made of all the observations. Scores before and after treatment was compared. It was found that there was vast difference in scores before and after treatment. Conclusions: Rhus tox was found to be effective in managing cases of Osteoarthritis of Knee Joint.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".