Effect of Local Heat Application on Complaints of Patients with Moderate Knee Osteoarthritis
Bibliographic record
Abstract
Background: Osteoarthritis (OA) brings discomfort and disability for around 10% of the total human population due to chronic joint pain. Heat therapy is a common pain management device and easy way to alleviate joints stiffness. This study aimed to evaluate the effect of local heat application on joint pain, stiffness, and physical function of patients with moderate knee OA. A quasi-experimental design was utilized. Setting: This study was conducted at the Outpatient Clinics related to the Orthopedic and Traumatology Hospital (El Hadara), Alexandria University, Egypt. Subject: a total of 52 patients with moderate knee OA were recruited as a convenience sample. They were enrolled into control and intervention groups (26 patients, each). Tools: four tools were utilized, Tool 1: to assess the severity of disease. Tool 2: Self reporting rating scales, to assess pain and tenderness pre and post heat applications. Tool 3: Western Ontario and McMaster (WOMAC) OA Index, which aims to determine the change(s) in daily function difficulties with pain, stiffness and physical function and Tool 4: Clinical physical assessment. Results: The results of the study showed high statistical significant differences in pain intensity and tenderness scores before and after applying hot compresses in the intervention group and also, between the control and intervention subjects regarding pain intensity and tenderness 4 weeks post heat applications. Statistical significant differences were found in control and intervention subjects post 4 weeks of intervention regarding scores of pain, joint stiffness, physical function disabilities, and all overall WOMAC. All studied subjects had body mass index score of > 27kg/m2. There were positive statistical significant correlations between pain intensity, tenderness, physical function, and Overall WOMAC scores and BMI in both control and intervention subjects (P≤ 0.05). Conclusion: local heat applications with moderately knee OA patients every other day decreased pain, stiffness and physical functional disability. Recommendations: additional randomized controlled trials are needed to evaluate long term heat application effects and follow up of patients with mild moderate and severe knee OA, are to be continued.
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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 | 0.001 |
| 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".