Impact of physiotherapy-led bike fitting on the evolution of knee pain in recreational cyclists: the PBF study
Bibliographic record
Abstract
INTRODUCTION: A Physiotherapy-led Bike Fitting (PBF) intervention including a bike fit, education, and exercise prescription can be helpful among cyclists with knee pain. OBJECTIVE: To describe the PBF intervention and to assess knee-related pain and disability change among recreational cyclists exposed to the PBF intervention. METHODS: This is a single group prospective observational longitudinal study on a cohort of recreational road cyclists who consulted for cycling-related knee pain at a physiotherapy clinic specialized in cycling. The PBF included a comprehensive bike fit focusing on key measurements such as knee flexion and knee alignment relative to the pedal axis while cycling. Additionally, tailored education was provided on cycling cadence and training progression, along with exercise prescriptions. The primary outcome was the knee pain during cycling measured with the numerical pain rating scale (NPRS; 0-10). Linear models were used to assess within-group changes across time points at 4 and 12 weeks. RESULTS: < .001) with respective improvements of -2.52 (95% CI: -3.04; -2), -0.7 (95% CI: -1.02; -0.38) and -1.81 (95% CI: -2.27; -1.36) at 12 weeks. CONCLUSIONS: Based on this single group observational study, recreational road cyclist exposed to a PBF intervention, including a bike fit, tailored education, and exercises prescriptions reported a reduction in cycling-related knee pain and disability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".