Risk factors for patellofemoral pain: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Patellofemoral pain (PFP) is a prevalent condition commencing at various points throughout life. We aimed to provide an evidence synthesis concerning predictive variables for PFP, to aid development of preventative interventions. METHODS: We searched Medline, Web of Science and SCOPUS until February 2017 for prospective studies investigating at least one potential risk factor for future PFP. Two independent reviewers appraised methodological quality using the Newcastle-Ottawa Scale. We conducted meta-analysis where appropriate, with standardised mean differences (SMD) and risk ratios calculated for continuous and nominal scaled data. RESULTS: This review included 18 studies involving 4818 participants, of whom 483 developed PFP (heterogeneous incidence 10%). Three distinct subgroups (military recruits, adolescents and recreational runners) were identified. Strong to moderate evidence indicated that age, height, weight, body mass index (BMI), body fat and Q angle were not risk factors for future PFP. Moderate evidence indicated that quadriceps weakness was a risk factor for future PFP in the military, especially when normalised by BMI (SMD -0.69, CI -1.02, -0.35). Moderate evidence indicated that hip weakness was not a risk factor for future PFP (multiple pooled SMDs, range -0.09 to -0.20), but in adolescents, moderate evidence indicated that increased hip abduction strength was a risk factor for future PFP (SMD 0.71, CI 0.39, 1.04). CONCLUSIONS: This review identified multiple variables that did not predict future PFP, but quadriceps weakness in military recruits and higher hip strength in adolescents were risk factors for PFP. Identifying modifiable risk factors is an urgent priority to improve prevention and treatment outcomes.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".