Finite Element Analysis Examining the Effects of Cam FAI on Hip Joint Mechanical Loading Using Subject-Specific Geometries during Standing and Maximum Squat
Bibliographic record
Abstract
BACKGROUND: Cam femoroacetabular impingement (FAI) can impose elevated mechanical loading in the hip, potentially leading to an eventual mechanical failure of the joint. Since in vivo data on the pathomechanisms of FAI are limited, it is still unclear how this deformity leads to osteoarthritis. PURPOSE: The purpose of this study was to examine the effects of cam FAI on hip joint mechanical loading using finite element analysis, by incorporating subject-specific geometries, kinematics, and kinetics. QUESTIONS: The research objectives were to address and determine: (1) if hips with cam FAI demonstrate higher maximum shear stresses, in comparison with control hips; (2) the magnitude of the peak maximum shear stresses; and (3) the locations of the peak maximum shear stresses. METHODS: Using finite element analysis, two patient models were control-matched and simulated during quasi-static positions from standing to squatting. Intersegmental hip forces, from a previous study, were applied to the subject-specific hip geometries, segmented from CT data, to evaluate the maximum shear stresses on the acetabular cartilage and underlying bone. RESULTS: Peak maximum shear stresses were found at the anterosuperior region of the underlying bone during squatting. The peaks at the anterosuperior acetabulum were substantially higher for the patients (15.2 ± 1.8 MPa) in comparison with the controls (4.5 ± 0.1 MPa). CONCLUSIONS: Peaks were not situated on the cartilage, but instead located on the underlying bone. The results correspond with the locations of initial cartilage degradation observed during surgical treatment and from MRI. CLINICAL RELEVANCE: These findings support the pathomechanism of cam FAI. Changes may originate from the underlying subchondral bone properties rather than direct shear stresses to the articular cartilage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".