Using Dual-Orthogonal Fluoroscopy and CT to Assess the Relationship Between Knee Morphology and Patellar Kinematics in Patients With Patellofemoral Pain
Bibliographic record
Abstract
INTRODUCTION: Patellofemoral pain (PFP) is one of the most common knee overuse injuries, with studies suggesting PFP as a precursor for early knee osteoarthritis. The etiology of PFP is multi-factorial; however, patellar mal-tracking has been regarded as a primary mechanism. Details of this multi-factorial mechanism have been unclear because of the limitations in evaluating in-vivo, three-dimensional (3D) patellofemoral joint movement during dynamic activities accurately. Alternatively, studies have demonstrated the high accuracy and repeatability of dual fluoroscopy and CT/MRI for measuring knee joint motion. OBJECTIVE: This study uses dual fluoroscopy and CT to investigate the associations between joint morphology and patellar kinematics in healthy controls and subjects with PFP. METHODS: Eight PFP females (29.7±10.6 years) and 10 healthy females (25.0±7.7 years) were recruited and screened by a sports medicine physician. CT imaging was performed on participants in a supine with the knee extended, and ankle and hip in neutral alignment. Dual-orthogonal fluoroscopy measured patellar movement while participants performed a lunge task. A calibration algorithm was used to register the 3D CT model to 2D fluoroscopy image to calculate the relative position and angles of the patella based on the clinical definition of patellar motion. Measures of patellar and trochlear morphology were generated and correlated to kinematic data. RESULTS AND CONCLUSION: There was a significant difference in the patellar-to-trochlear width ratio; however, no other significant differences in CT morphology measurements were present between groups. For PFP patients in the weight-bearing extended position, there was a moderate positive correlation between the patellar-to-trochlear width ratio and medial-lateral patellar shift (τ = 0.643, p = 0.026). Healthy controls in this position demonstrated a moderate positive correlation between the lateral-trochlear inclination angle and medial-lateral patellar shift (τ = 0.600, p = 0.016) and moderate negative correlation between medial trochlear inclination angle and medial-lateral patellar shift (τ = -0.511, p = 0.040). The findings suggest that, for this cohort, there is correlation between morphology and patellar kinematics. Passive and active stabilizers likely have a role in mal-tracking.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".