Correlation of lower limb muscles strength and flexibility with pain and function in females with Patellofemoral Pain
Bibliographic record
Abstract
Introduction: Reduced strength and flexibility of lower limb muscles have been proposed as contributing factors to Patellofemoral Pain (PFP). However, relationship of muscle strength and flexibility with pain and function in people with PFP is not well recognized, yet. The purpose of this study was to investigate the correlation of lower limb muscles strength and flexibility with pain and function in females with PFP.\n\nMaterials and Methods: In this cross-sectional study 35 females with PFP and 35 matched healthy females participated. Muscle strength, muscle flexibility, and pain were assessed using a dynamometer, an inclinometer, and visual analog scale, respectively. In this account,Kujalla questionnaire and step-down test were used to determine the function. The data were analyzed using an independent t -test, Pearson correlation test and stepwise regression.\n\nResults: There were significantly lower strength of the knee extensors, the knee flexors, the hip abductors and lateral rotators in the PFP group as compared to the healthy group (P <0.001). The hip medial rotators, illiotibial band and quadriceps flexibility in the PFP group were significantly lower compared with the healthy group (P <0.05). Based on regression analysis, quadriceps flexibility and hip abductor muscle strength may predict Kujalla (R2=0.20) and step-down scores (R2=0.16) in females with PFP. No significant correlation was found between pain intensity, function and muscle strength.\n\nConclusion: lower limb muscles strength in females with PFP is significantly less than a healthy matched group. Qquadriceps muscle flexibility and the hip abductor muscles strength may predict Kujalla and step-down scores in females with PFP, respectively
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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) | 0.004 | 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".