Medical Interventions for Patellofemoral Pain and Patellofemoral Osteoarthritis: A Systematic Review
Bibliographic record
Abstract
Patellofemoral pain (PFP) and patellofemoral osteoarthritis (PFOA) are common, persistent conditions that may lie along a pathological spectrum. While evidence supports exercise-therapy as a core treatment for PFP and PFOA, primary care physicians commonly prescribe medication, or refer for surgical consults in persistent cases. We conducted a systematic review of medical interventions (pharmaceutical, nutraceutical, and surgical) for PFP and PFOA to inform primary care decision making. Methods: Following protocol registration, we searched seven databases for randomized clinical trials of our target interventions for PFP and PFOA. Our primary outcome was pain. We assessed risk of bias, calculated standardized mean differences (SMDs) and determined the level of evidence for each intervention. Results: We included 14 publications investigating pharmaceutical or nutraceutical interventions, and eight publications investigating surgical interventions. Two randomized control trials (RCTs) provided moderate evidence of patellofemoral arthroplasty having similar pain outcomes compared to total knee arthroplasty in isolated PFOA, with SMDs ranging from −0.3 (95% CI −0.8, 0.2, Western Ontario McMaster Pain Subscale, 1 year post-surgery) to 0.3 (−0.1, 0.7, SF-36 Bodily Pain, 2 years post-surgery). Remaining studies provided, at most, limited evidence. No efficacy was demonstrated for oral nonsteroidal anti-inflammatories or arthroscopic surgery. Conclusions: Pharmaceutical and nutraceutical prescriptions, and surgical referrals are currently being made with little supporting evidence, with some interventions showing limited efficacy. This should be considered within the broader context of evidence supporting exercise-therapy as a core treatment for PFP and PFOA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".