The Effect of Hydrotherapy Exercise on Pain Intensity and Functional Ability in Genu Osteoarthritis Patients
Bibliographic record
Abstract
Background: Genu osteoarthritis (OA) is the most common arthritis disease caused by a degenerative joint process that causes knee pain and functional disorders. One of the recommended therapies for the treatment of OA genu from several studies is hydrotherapy exercises. Hydrotherapy exercises can reduce pain and improve the patient's quality of life. This study aims to determine the effect of hydrothera py exercises on pain intensity and functional ability in OA genu patients. Methods: This study is a quasi-experimental study with a one group pretest-posttest design concted by the Medical Rehabilitation Installation of dr. Mohammad Hoesin Palembang duri ng the month of October-November 2019. Primary data was collected using interviews to assess pain intensity based on the Numerical Paon Rating Scale (NPRS) score and functional ability based on the WOMAC (Western Ontario and McMaster Universiti es Osteoarthritis Index) questionnaire before and after training. hydrotherapy for 4 weeks for one therapy a week. The data was carried out by the Shapiro-Wilk normality test, then analyzed using the paired T -test or Wilcoxon test using the SPSS tool. Results: Asmany as 31 study subjects, the results showed that there was a significant effect before and after hydrotherapy exercises on reducing pain intensity (p = 0.000) and improving functional ability (p = 0.000) in patients with OA genu for 4 weeks. Conclusion: There is an effect of hydrotherapy exercise on pain intensity and functional ability in genu.
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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) | 0.003 | 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".