Effect of low-intensity plyometrics and eccentric training programs on pain, strength and function in patients with knee osteoarthritis: A randomized control trial protocol
Bibliographic record
Abstract
Meniscus degeneration, synovial inflammation, subchondral bone changes, and cartilage loss serve as the best indicators of osteoarthritis (OA). The most prevalent type of joint conditions, OA, impairs mobility, lowers quality of life, and limits participation in social activities. Although pain is the primary concern for the majority of patients, clinical symptoms also include joint stiffness, discomfort, and dysfunction. There is enough data to draw the conclusion that physiotherapy treatments can reduce knee OA patients’ pain and enhance their functional capabilities. Two treatment methods that are particularly effective and advantageous for people with knee OA are plyometrics and eccentric training programmes. Our study will compare the impact of eccentric training programmes and low-intensity plyometric training programmed on pain, strength, and function in patients with Grade 1 and Grade 2 knee OA. In this study, the following outcome measures will be utilised: the Visual Analogue Scale (VAS), the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the Two Minute Walk Test, as our final performance measures for pain and function, respectively. We will determine strength by using portable hand-held dynamometers. Through this study, we will be able to create a plyometric training regimen that can be given to individuals with knee osteoarthritis to improve their physical well-being and athletic performance. These training programmes would be highly effective in such patients, in addition to conventional treatment. Registration number: CTRI/2023/06/053657
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".