To See Changes Following Physiotherapy in Pain, Range of Motion, Muscle Strength And Functional Performance in Patients with Osteoarthritis of Knee
Bibliographic record
Abstract
Background: Osteoarthritis (OA) is a chronic, non-inflammatory, degenerative joint disease with Knee OA being the most prevalent one with main factor of disability among middle-aged and older people worldwide. Age and repetitive mechanical loads being the main causative factors, it shows pain Knee range to be painful. Techniques like IFT and wet packs, physiotherapy seeks to reduce signs and symptoms including pain and oedema. Non-weight-bearing muscle-strengthening exercises have been shown to be useful. Methodology: A Case series was carried out by the Interns on Patients having osteoarthritis of knee who visited Physiotherapy OPD, Dhiraj Hospital, with a minimal sample size of 5 cases, for the study duration of 3 Months. The Inclusion Criteria for the study were: Both Male and Female patients with Age 40-70 years, Patients diagnosed with Unilateral/bilateral OA knee, willing to perform physical therapy exercise, having Kellgren-Lawrence Grade 1 & 2 graded by radiologist. Outcome Measures used were: Numeric Pain Rating Scale, Western Ontario and McMaster universities arthritis index (WOMAC), Repetition Maximum (RM), Goniometery. Result: The results showed decrease in pain, Increase ROM of knee ranges, improve strength of the muscles and better functional performance Conclusion: It was observed that Physiotherapy improved knee range of motion, muscle strength, pain and physical function in patient with osteoarthritis of knee.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".