The Effectiveness of Ultrasound (US) Intervention and Static Bicycle Exercise to Improve Functional Ability in Genu Osteoarthritis
Bibliographic record
Abstract
Genu Osteoarthritis is a degenerative disease of the knee joint due to the abrasion of joint cartilage and the formation of new bone on the joint surface which can cause muscle and tendon weakness so that movement is limited and causes pain. Degenerative diseases of the genu can cause the erosion of the articular surface, this will cause pain and swelling in the genu. This research method is pure experimental with randomized pre-test and post-test group design. Samples of this study is 13 patients were given Ultrasound (US) and Static Bicycle Exercise 6 times for 2 weeks. Physiotherapy examinations and measurements were carried out using the WOMAC Index (Western Ontario and McMaster Universities Osteoarthritis Index). The results showed an increase in knee functional activity before and after treatments with the normality test using the Shapiro-Wilk test with value P value before = 0.034 (P>0.05) with a mean value of 78.00± 4.46 and the P value after = 0.276 (P>0.05) with a mean of 19.77±5.85 then the normality value of the data is normally distributed so the research hypothesis test used Paired Samples Test with significant results P = 0.000 (P<0, 05) with a mean value = 58.23±5.49. The conclusion of this study is Ultrasound (US) and Static Bicycle Exercise can improve functional ability in patients with Genu Osteoarthritis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".