Lasting Improvement of Patient-Reported Outcomes 6 Months After Patellofemoral Pain Rehabilitation
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Hip- and knee-muscle-strengthening programs are effective in improving short-term patient-reported and disease-oriented outcomes in individuals with patellofemoral pain (PFP), but few to no data exist on moderate- to long-term postrehabilitative outcomes. The first purpose of the study was to assess differences in pain, function, strength, and core endurance in individuals with PFP before, after, and 6 mo after successful hip- or knee-muscle-strengthening rehabilitation. The second purpose was to prospectively follow these subjects for PFP recurrence at 6, 12, and 24 mo postrehabilitation. METHODS: For 24 mo postrehabilitation, 157 physically active subjects with PFP who reported treatment success were followed. At 6 mo postrehabilitation, pain, function, hip and knee strength, and core endurance were measured. At 6, 12, 18, and 24 mo, PFP recurrence was measured via electronic surveys. RESULTS: Sixty-eight subjects (43%) returned to the laboratory at 6 mo. Regardless of rehabilitation program, subjects experienced significant improvements in pain and function, strength, and core endurance pre- to postrehabilitation and maintained improvements in pain and function 6 mo postrehabilitation (Visual Analog Scale/Pain-pre 5.12 ± 1.33, post 1.28 ± 1.14, 6 mo 1.68 ± 2.16 cm, P < .05; Anterior Knee Pain Scale/Function-pre 76.38 ± 8.42, post 92.77 ± 7.36, 6 mo 90.27 ± 9.46 points, P < .05). Over the 24 mo postrehabilitation, 5.10% of subjects who responded to the surveys reported PFP recurrence. CONCLUSIONS: The findings support implementing a hip-or knee-muscle-strengthening program for the treatment of PFP. Both programs improve pain, function, strength, and core endurance in the short term with moderate- and long-term benefits of improved pain and function and low PFP recurrence.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".