Effect of Tibial Torsion and Pronation-specific Strengthening Exercises on Pain and Functional Limitations among Acute Osteoarthritis Knee Patients
Bibliographic record
Abstract
A BSTRACT Introduction: Knee osteoarthritis (OA) has a 16% global prevalence and an incidence of approximately 203 cases per 10,000 people, indicating a global health burden. Only a few studies have addressed the biomechanical malalignments, the reasons for deviations, and gait abnormalities that developed due to OA progression. The study aimed to determine the effect of tibial torsion and pronation-specific strengthening exercises on pain and functional limitations among acute OA knee patients. Subjects and Methods: We selected 30 subjects, aged 35–50 years for an experimental study with pre- and posttest study design, based on inclusion and exclusion criteria. Fifteen patients were recruited for each group. Group A trained with torsion and pronation-specific exercises and conventional exercises for 8 weeks, and Group B trained with therapeutic conventional exercises alone. Results: There is a significant difference in the Visual Analog Scale score pretest values (5.60 ± 1.12) and post values (3.33 ± 1.04) between the groups with P < 0.05 by the torsion and pronation-specific exercises, whereas there is no significant difference between the groups (Groups A and B) in functional limitations results with a P = 1. Western Ontario and McMaster Universities Arthritis Index score pretest values (1.60 ± 0.50) and posttest values (1.27 ± 0.45). Conclusion: The study concluded that tibial torsion and pronation-specific strengthening exercises are effective treatment approaches for reducing pain and thereby functional limitation can be prevented in the earlier phase among acute OA patients compared to therapeutic conventional exercises.
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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 | 0.001 |
| 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) | 0.003 | 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".