Effectiveness of Transcutaneous Electrical Nerve Stimulation and strengthening exercises on the functional ability of patients with osteoarthritis of the knee joints: A case report
Bibliographic record
Abstract
Knee osteoarthritis (OA) is the most common musculoskeletal disease among elderly individuals. It affects the ability to sit on a chair, stand, walk, and climb stairs. Our objective was to evaluate the effectiveness of tens and strengthening exercises on functional ability in patients with OA of knee joints. We present a case of a 55-year-old female housewife with bilateral knee pain, which manifested suddenly as a dull ache exacerbated by walking and stair climbing and alleviated by rest. Clinical examination revealed crepitus, grade 1 tenderness, and reduced range of motion (ROM) in the left knee. The radiographic evaluation confirmed grade 3 osteoarthritis according to the Kellgren and Lawrence grading system, indicating moderate multiple osteophytes, definite joint space narrowing, some sclerosis, and potential bone deformity. The patient underwent a 4-week intervention comprising strengthening exercises and TENS. Pain intensity was assessed using the Numeric Pain Rating Scale (NPRS), knee ROM with a goniometer, and functional limitations with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Assessments were conducted weekly throughout the intervention. The findings suggest that the combined application of TENS and strengthening exercises effectively alleviates pain and improves functional ability in patients with knee osteoarthritis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".