Treatment Success of Hip and Core or Knee Strengthening for Patellofemoral Pain: Development of Clinical Prediction Rules
Bibliographic record
Abstract
CONTEXT: Patellofemoral pain (PFP) is a common injury that interferes with quality of life and physical activity. Clinical subgroups of patients may exist, one of which is caused by proximal muscle dysfunction. OBJECTIVES: To develop clinical prediction rules that predict a positive outcome after either a hip and core- or knee-focused strengthening program for individuals with PFP. DESIGN: Secondary analysis of data from a randomized control trial. SETTING: Four university laboratories. PATIENTS OR OTHER PARTICIPANTS: A total of 199 participants with PFP. INTERVENTION(S): Participants were randomly allocated to either a hip and core-focused (n = 111) or knee-focused (n = 88) rehabilitation group for a 6-week program. MAIN OUTCOME MEASURE(S): Demographics, self-reported knee pain (visual analog scale) and function (Anterior Knee Pain Scale), hip strength, abdominal muscle endurance, and hip range of motion were evaluated at baseline. Treatment success was defined as a decrease in visual analog scale score by ≥2 cm or an increase in the Anterior Knee Pain Scale score by ≥8 points or both. Bivariate relationships between the outcome (treatment success) and the predictor variables were explored, followed by a forward stepwise logistic regression to predict a successful outcome. RESULTS: Patients with more pain, better function, greater lateral core endurance, and less anterior core endurance were more likely to have a successful outcome after hip and core strengthening (88% sensitivity and 54% specificity). Patients with lower weight, weaker hip internal rotation, stronger hip extension, and greater trunk-extension endurance were more likely to have success after knee strengthening (82% sensitivity and 58% specificity). CONCLUSION: The patients with PFP who have more baseline pain and yet maintain a high level of function may experience additional benefit from hip and core strengthening. The clinical prediction rules from this study remain in the developmental phase and should be applied with caution until externally validated.
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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 | 0.023 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".