A Contemporary Approach to Patellofemoral Pain in Runners
Bibliographic record
Abstract
Patellofemoral pain (PFP) is among the most common injuries in recreational runners. Current evidence does not identify alignment, muscle weakness, and patellar maltracking or a combination of these as causes of PFP. Rather than solely investigating biomechanics, we suggest a holistic approach to address the causes of PFP. Both external loads, such as changes in training parameters and biomechanics, and internal loads, such as sleep and psychological stress, should be considered. As for the management of runners with PFP, recent research suggested that various interventions can be considered to help symptoms, even if these interventions target biomechanical factors that may not have caused the injury in the first place. In this Current Concepts article, we describe how the latest evidence on education about training modifications, strengthening exercises, gait and footwear modifications, and psychosocial factors can be applied when treating runners with PFP. The importance of maintaining relative homeostasis between load and capacity will be emphasized. Recommendations for temporary or longer-term interventions will be discussed. A holistic, evidence-based approach should consist of a graded exposure to load, including movement, exercise, and running, while considering the capacity of the individual, including sleep and psychosocial factors. Cost, accessibility, and the personal preferences of patients should also be considered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".