Effectiveness of knee exercises versus combined knee and hip exercises in treatment of patellofemoral pain: A randomized clinical trial
Bibliographic record
Abstract
Introduction: Previous studies suggest that hip muscle weakness may contribute to patellofemoral pain (PFP). Accordingly, addition of hip strengthening exercises to conventional knee exercises was recommended for treatment of PFP. However, evidence to support superior efficacy of additional hip exercises in treatment of PFP is limited. This study compared the clinical efficacy of knee exercises versus combined knee and hip exercises in females with PFP.\n\nMaterials and Methods: In this randomized clinical trial 60 females with PFP were randomly assigned into two groups: “the knee exercises” and “the knee and hip exercises”. Participants performed progressive therapeutic exercises 3 times a week for 4 weeks. Pain, muscle strength (the knee extensors, the hip abductors and the hip external rotators) and physical function were evaluated before and after treatment interventions using Visual Analogue Scale (VAS), a dynamometer, and step-down test, respectively.\n\nResults: Both groups showed significant improvements in pain, function, and the knee extensor, hip abductor and hip external rotator muscles strength after the interventions (P<0.001). There were no significant differences in muscle strength, pain and function between the groups (P>0.05).\n\nConclusion: Four weeks of either knee exercises or combined knee and hip exercises significantly improve function and reduce pain in women with PFP. Addition of hip strengthening exercises to conventional knee exercises was not associated with superior treatment outcomes.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".