The Association Between Knee Biomechanics During Running And Resting Trochlea Cartilage Thickness
Bibliographic record
Abstract
There is ambiguity in the role of loading on knee cartilage development and degeneration in distance runners. PURPOSE: To examine the association between knee joint kinetics during running and resting cartilage thickness. It was hypothesized that greater joint kinetics would be associated with thicker resting cartilage. METHODS: Sixteen runners participated (7 females and 9 males; Height = 1.73 ± 0.07 m, Mass = 66.7 ± 8.3 kg, Age = 21.5 ± 3.7 years, self-selected running Speed = 3.34 ± 0.4 m/s, Weekly Running Amount = 51.9 ± 25.7 km, Running Experience = 6.53 ± 3.5 years). Runners were defined as anyone who runs at least 3 times per week, and 16 kilometers in that week for the past 6 months. Participants rested supine for 30 minutes before ultrasound imaging of trochlear cartilage. After imaging, participants ran at a self-selected speed for 35 minutes on a force instrumented treadmill while kinetics and kinematics were recorded at 2400 Hz and 240 Hz, respectively. Variables of interest included peak patellofemoral joint stress (PFJS), peak patellofemoral joint force (PFJF), peak knee flexion angle (KFA), peak knee extensor moment (KEM), medial, central, and lateral cartilage region thickness and total thickness of femoral trochlea cartilage. Spearman’s rho assessed the association between biomechanical and cartilage outcomes (α = 0.05). RESULTS: Peak PFJS was associated with medial (rho = 0.653, p = 0.006), central (rho = 0.503, p = 0.047), lateral (rho = 0.529, p = 0.035) and total (rho = 0.618, p = 0.011) cartilage thickness. Peak PFJF was associated with medial cartilage thickness (rho = 0.524, p = 0.037) but not central, lateral, or total cartilage thickness (all rho<0.32, all p > 0.05). Peak KEM and peak KFA were not associated with medial, central and lateral cartilage thickness and total cartilage thickness (all rho<0.48, all p > 0.06). CONCLUSIONS: Peak PFJS was consistently associated with all cartilage outcomes, but PFJF, KEM and KFA were not. Therefore, robust biomechanical modeling is useful when quantifying knee loading characteristics. Greater PFJS during running may provide a beneficial mechanical stimulus for cartilage maintenance and development. Supported by: This project received funding from the Natural Science and Engineering Research Council of Canada (RGPIN-2022-04804, PI: DN Pamukoff), the Canadian Foundation for Innovation John R. Evans Leader Fund (Project ID: 42110, PI: DN Pamukoff) and the Ontario Graduate Scholarship 2024-2025 (RJ Evans).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".