Effectiveness of Epsom Salt with Hot Water Application on Knee Joint Pain among Elderly in a Selected Rural Area at Puducherry
Bibliographic record
Abstract
Osteoarthritis (OA) is a disease of cartilage degradation, which results pain in major joints, especially in knee joint.Globally, OA ranks eighth in all diseases and covers around 15% proportions among all musculoskeletal problems.Background: Osteoarthritis is a disease of the cartilage which leads to degradation and results in pain in the major joints, especially in the knee joints.Knee joint pain is the most frequent complaint among the geriatric population.The objectives of this study were to assess the level of pain in knee joint among elderly, to evaluate the effectiveness of hot water application with Epsom salt on knee joint pain among elderly, and to find out the association between the level of pain in knee joint and selected demographic variables.Materials and methods: A preexperimental research design was adopted for this study.This study was conducted among elderly aged above 60 years residing in T.N.Palayam.In total, 29 samples of elderly aged above 60 years residing in T.N.Palayam were selected based on the purposive sampling technique.The demographic data were collected from the elderly and then the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) oesteoarthritis rating scale was used to assess the degree of pain.Results: The result revealed that out of 29 samples, pretest mean score level of pain was 2.93 with the standard deviation of 0.593, whereas after implementation of Epsom salt with hot water application, the posttest mean pain score was 2.17 with the standard deviation of 0.384.The effectiveness was statistically tested by paired t test which was found to be highly statistically significant at p less than 0.001.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".