FINITE ELEMENT ANALYSIS OF THE IMPINGING HIP: SUBJECT-SPECIFIC MODELS TO ASSESS THE RISK OF OSTEOARTHRITIC DEGENERATION
Bibliographic record
Abstract
Femoro-acetabular impingement involves a deformity of the hip joint and is associated with hip osteoarthritis. Although 15% of the asymptomatic population exhibits a deformity, it is not clear who will develop symptoms. Current diagnostic imaging measures have either low specificity or low sensitivity and do not consider the dynamic nature of impingement during daily activities. The goal of this study is to determine stresses in the cartilage, subchondral bone and labrum of normal and impinging hips during activities such as walking and sitting down. Quantitative CT scans were obtained of a healthy Control and a participant with a symptomatic femoral cam deformity (‘Bump’). 3D models of the hip were created from automatic segmentation of CT scans. Cartilage layers were added so the articular surface was the mid-line of the joint. Finite element meshes were generated in each region. Bone elastic modulus was assigned element-by-element, calculated from CT intensity converted to bone mineral density using a calibration phantom. Cartilage was modelled as poroelastic, E=0.467 MPa, v=0.167, and permeability 3×10-16 m4/N s. The pelvis was fixed while rotations and contact forces from Bergmann et al. (2001) were applied to the femur over one load cycle for walking and sitting in a chair. All analyses were performed in FEBio. High shear stresses were seen near the acetabular cartilage-labrum junction in the Bump model, up to 0.12 MPa for walking and were much higher than in the Control. Patient-specific modelling can be used to assess contact and tissue stresses during different activities to better understand the risk of degeneration in individuals, especially for activities that involve high hip flexion. The high stresses at the cartilage labrum interface could explain so-called bucket-handle tears of the labrum.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".