Effect of Physical Therapy Treatment in Knee Osteoarthritis with and without Wedge Insole: Randomized Controlled Trial
Bibliographic record
Abstract
The purpose of this study was to determine the effects of physical therapy treatment in knee osteoarthritis with and without wedge support.Methods: This randomized controlled trail was conducted at Outpatient Department of Physical Therapy, Mayo Hospital, Lahore and Mid City Hospital, Lahore. Eighty patients with knee osteoarthritis were selected using a predefined inclusion and exclusion criteria and randomly allocated in two treatment groups; Group A (Routine physiotherapy treatment) and Group B (Routine physiotherapy with wedge insole). Outcome were recorded at the start and at the end of treatment at 3 weeks in terms of pain and disability as recorded on Numeric Pain Rating Scale (NPRS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) respectively. The collected data was entered and analyzed using SPSS v25 with p value < 0.05 as significant.Results: At the end of the third week, there was a statistically significant difference between the two treatment groups in terms of VAS (Mean Difference 1.55; p value 0.05) and WOMAC (Mean Difference 5.53; p value 0.0001). A statistically significant difference in before and after treatment VAS and WOMAC score was also noted within each group with p value <0.0001.Conclusion: In comparison to conventional physical therapy alone, adding wedge insoles to the routine physical therapy treatment of Knee Osteoarthritis results in better pain relief and function improvement.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".