Effectiveness of Specific Training on Physical Functional Improvement and Walking Speed in Patients with Knee Osteoarthritis
Bibliographic record
Abstract
Background: Osteoarthritis (OA) is a musculoskeletal disorder which is a progressive change in joints that is slow and intermittent, usually in the field there are exercises to strengthen muscles which are only supported by the agonist muscles while the antagonistic muscles also participate and even more dominant, the most approved intervention to be able to activate both muscles is a special task. The purpose of this study is to study whether there are functional requirements for specific tasks to improve the functioning and running of osteoarthritis patients.Subjects and Method: This study method uses the type of experimental research with a single case research method using A-B-A research design. The study was conducted in January 2020 located at the University of Muhammadiyah Surakarta. A patient with knee OA was selected using consecutive sampling. Functional ability and walking speed were tested by West Ontario and McMaster Osteoarthritis Index (WOMAC).Results: Specific training improved physical function and speed walking in patients with knee osteoarthritis. There was no effect of specific training on pain and stiffness.Conclusion: Task specific training improves functional ability and walking performance in aptient with knee osteoarthritis.Keywords: Osteoarthritis, task specific training, visual surface electromyograph, augmented feedback, functional ability.Correspondence: Suryo Saputra Perdana. Faculty of Health Sciences, Universitas Muhammadiyah Surakarta, Jl. Ahmad Yani, Tromol Pos 1, Pabelan Kartasura, Sukoharjo 57169, Central Java, Indonesia. Phone: +6281298563988. Email: suryo.saputra@ums.ac.id.Indonesian Journal of Medicine (2022), 07(01): 89-101https://doi.org/10.26911/theijmed.2022.07.01.10
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".