Is patellofemoral pain preventable? A systematic review and meta-analysis of randomised controlled trials
Bibliographic record
Abstract
Objective To evaluate the effectiveness of interventions to reduce the risk of incident patellofemoral pain. Design Systematic review and meta-analysis, with strength of evidence evaluated separately for each intervention type. Data sources MEDLINE, EMBASE, CINAHL, Web of Science and SPORTDiscus. Eligibility criteria for selecting studies Randomised controlled trials evaluating the effectiveness of interventions to reduce patellofemoral pain risk compared with a control/non-exposed group. Results Thirteen trials of mostly military recruits and young athletes analysed six different interventions. There was low certainty evidence from two trials (227 participants) that patellofemoral braces worn during physical activity (compared with no brace) effectively reduced the risk of patellofemoral pain (risk ratio (RR) 0.40, 95% CI 0.22 to 0.73; I 2 =24.0%). There was low certainty evidence from one trial (320 participants) that running technique retraining to (run softer) reduced patellofemoral pain risk (RR 0.21, 95% CI 0.07 to 0.60). There was low certainty evidence from four trials (3364 participants) that multicomponent (strengthening/neuromuscular) exercise programmes did not significantly reduce the risk of patellofemoral pain (RR 0.49, 95% CI 0.18 to 1.36; I 2 =64.9%), although broad CIs may reflect exercise dose variations among studies. There was very low certainty evidence from four trials (2314 participants) that foot orthoses (compared with flat inserts/no orthosis) did not significantly reduce the risk of patellofemoral pain (RR 0.63, 95% CI 0.35 to 1.13; I 2 =0.0%). Static stretching and a running programme that progressed intensity (compared with volume) did not significantly influence patellofemoral pain risk (single studies). Conclusion There is low-level evidence that patellofemoral braces and running technique retraining can reduce the risk of patellofemoral pain by 60%–79%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.050 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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 teacher head, 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".