Analysis of pain and functional status of patients with knee osteoarthritis after transcutaneous electrical nerve stimulation treatment
Bibliographic record
Abstract
Objective. Evaluation of analgesic effect of transcutaneous electrical nerve stimulation (TENS) and functional status of patients with knee osteoarthritis in three intervals: immediately after treatment, one and three months after therapy. Methods. Sample consisted of 40 patients suffering from knee osteoarthritis, aged from 50-75 years. In examinees' evaluation, the same protocol was used for all patients: circumference of knees across the middle of patella, perimeter of knee movements, and rough muscle strength of quadriceps femoris muscle. Measurements were performed before and after therapy, after a month, and after three months. The questionnaire of pain, short form of McGill pain questionnaire, was filled in the same time interval. TENS procedures were applied five times of week, total of ten days. The same protocol of kinesitherapy treatment applied to all patients, uniformly. In statistical analysis we used descriptive methods and t-test. Results. There was an improvement of functional state concerning the decrease of circumference of knees, increase of perimeter of knee movement as well as an increase of rough muscle strength, with the greatest effect after three months (after the end of therapy). The greatest analgesic effect was after a month of applied treatment protocol. Conclusion. Application of transcutaneous electrical nerve stimulation, as simple and non-invasive procedures in treatment patients with knee osteoarthritis together with continuous application of kinesitherapy, improve the functional status as well as the better quality of life of the patient.
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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".