Effectiveness of Static Quadriceps Stretching in Individuals With Patellofemoral Joint Pain
Bibliographic record
Abstract
OBJECTIVE: To determine if a 3-week static stretching program would increase quadriceps muscle flexibility in patellofemoral joint pain syndrome (PJPS) individuals. DESIGN: Pretest/posttest control group design. SETTING: Institutional based clinical rehabilitation setting. PARTICIPANTS: Participants (n = 83) were between the ages of 18 and 45 years of age with no history of surgery or trauma to the hip, knee, or lower leg region. INTERVENTION: Participants were sorted into normal and PJPS groups via orthopaedic assessment of knee pain and function, and their quadriceps flexibility was evaluated. All subjects completed a 3-week static quadriceps stretching program. Flexibility, knee pain, and function were then reassessed. MAIN OUTCOME MEASUREMENTS: Parametric and nonparametric tests were used to compare the groups' pre and poststretching knee pain, function, and quadriceps flexibility scores. RESULTS: Prestretching anthropometric and physical activity data illustrated that the groups were homogenous, with severity of knee pain, joint dysfunction, and quadriceps flexibility being the prime differences. Following the stretching program, a significant improvement in flexibility was detected for both groups, and the PJPS group reported a significant decrease in knee pain and improved joint function. However, Pearson product-moment correlation coefficients indicated that changes in quadriceps flexibility were poorly correlated with changes in knee pain and function. CONCLUSION: This study confirms the effectiveness of a 3-week static stretching regimen for enhancing quadriceps flexibility and knee joint function, but fails to demonstrate a statistical relationship between quadriceps flexibility and the severity of knee pain and joint dysfunction in a PJPS population.
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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) | 0.002 | 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".