The Effect of Foot Reflexology and Knee Massage With Black Cumin Seed Oil on Knee Osteoarthritis Symptoms
Bibliographic record
Abstract
This study aimed to examine the effect of foot reflexology and knee massage with black cumin seed oil on pain and fatigue symptoms in elderly individuals with knee osteoarthritis and assess which of these 2 applications is more effective. Our randomized controlled trial was conducted with 150 participants. Study data were collected from participants over 65 years who received outpatient treatment in a university hospital's physical therapy and rehabilitation unit and were determined to have no perception problems based on the Mini-Mental Test. After randomization, the study sample was classified into 5 groups, each including 30 participants: (1) foot reflexology with black cumin seed oil, (2) foot reflexology with a placebo, (3) knee massage with black cumin oil, (4) knee massage with a placebo, and (5) control. Participants were administered a Patient Descriptive Information Form, the Lequesne Knee Osteoarthritis Index, the Pain-Visual Analog Scale, the Fatigue Severity Scale, and the Western Ontario and McMaster Universities Osteoarthritis Index. Control group participants received standard of care, while participants in treatment groups received the studied interventions for 6 weeks. Data were collected by administering questionnaires to the participants in the first and sixth weeks and analyzed using IBM Statistical Package for Social Sciences 22.0 software. The study showed that foot reflexology and knee massage administered using black cumin oil effectively reduced pain and fatigue severity in Osteoarthritis (OA) patients, and overall, foot reflexology administered using black cumin oil was the most effective treatment to reduce pain and fatigue.
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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 | 0.001 | 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".