Should we consider changing traditional physiotherapy treatment of patellofemoral pain based on recent insights from the literature?
Bibliographic record
Abstract
The 2016 international patellofemoral pain (PFP) consensus statement1 suggested exercise therapy targeting the hip and knee, combined interventions and prefabricated foot orthoses can be used to improve pain and function in people with PFP. These recommendations are based on strong foundations including synthesis of multiple high-quality systematic reviews combined with voting from the International Patellofemoral Research Network group. A recent prognostic paper indicated that nearly 50% of people with PFP are likely to benefit from traditional physiotherapy in the longer term.2 However, 57% report unfavourable outcomes 5–8 years after being enrolled in a traditional physiotherapy clinical trial, indicating a need for alternative approaches in these individuals.2 Importantly, patient outcomes may be improved by providing interventions tailored to their needs. Efforts are under way to optimise subgrouping of patients in order to target traditional physiotherapy interventions. The purpose of this Editorial is a ‘call to action’ for researchers and clinicians (see box 1) to also consider exploring, incorporating and tailoring non-traditional physiotherapy interventions to optimise patient outcomes. Based on recent insights contained within two systematic reviews and one randomised clinical trial published in the British Journal of Sports Medicine , this may include weight management, addressing psychological factors …
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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 | 0.009 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".