Effectiveness of Light Pressure Stroking Massage with Sesame Oil in Alleviating Acute Knee Joint Pain among Elderly Adults
Bibliographic record
Abstract
Osteoarthritis (Acute knee joint pain) is a major health problem in the world. This investigation to assess the impact of lightweight stroking knead with effective sesame oil on torment seriousness of patients with knee joint agony. The main aim to determine the effectiveness of light pressure stroking massage with sesame oil on alleviating acute knee joint pain among elderly adults. (Age, Sex, Religion, Marital status, Educational status, Diet habit, Exercise, Body mass index). A pre-test was conducted to assess the WOMAC scale was managed to evaluate the level of pain. A back rub with sesame oil was applied 3weeks period. A post-test was led to evaluate the adequacy of the intercession. The knee pain level was surveyed by Western Ontario MAC Master Scale. The discoveries were most of the older grown-up individuals 46.7% had moderate pain, 10% had moderate pain, 31.7% had serious pain, 11.6% had extreme pain. After the back rub with sesame oil the knee pain level was diminished to 26.7% had mild pain, 40% had moderate pain, 25% had serious pain, 3% had extreme pain. The finding of the examination uncovered that knead with sesame oil for Knee joint pain was compelling in a huge decrease of the knee joint pain level at the degree of p<0.05 in the trial gathering. Thusly, because of ease, simple use and absence of unfriendly impact, it is proposed to utilize this oil on integral medication for relief from discomfort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| 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".