Core Training for Pain Management and Functional Improvement in Patients With Patellofemoral Pain Syndrome
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to investigate the effects of core strengthening exercises on pain, mobility, and lower limb muscle strength in patients with patellofemoral pain syndrome. DESIGN: Six databases were searched from inception until August 11, 2023. Pain, function, and muscle strength-related outcomes were extracted and the quality of the studies was assessed using the PEDro scale and the level of evidence was assessed using the GRADE. RESULTS: Nineteen studies involving 1138 patients were included. Very low-grade evidence supported the short-term pain-relieving effect of core training (standardized mean difference = -0.60, 95% confidence interval [-0.95, -0.25]), high-grade evidence supported the short-term functional improvement effects of core training (weighted mean difference = 3.61, 95% confidence interval [1.44, 5.78]), which was similarly significant within 3-12 mos of follow-up. The results of the subgroup analyses suggested that hip-knee training was most advantageous in relieving pain and enhancing motor function. CONCLUSIONS: Although training that includes trunk core is clearly superior to knee strengthening alone, the effectiveness of hip-knee training, which is also a core training program for pain and function, is more pronounced. The available evidence supports that hip-knee training is the most valuable treatment option for patients with patellofemoral pain syndrome.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".