Comparison of anatomical parameters of cam femoroacetabular impingement to evaluate hip joint models segmented from CT data
Bibliographic record
Abstract
Subject-specific anatomical finite element models obtained from three-dimensional (3D) segmentation have the potential to provide great insights into the pathomechanisms of femoroacetabular impingement (FAI). Still, the accuracy of the geometries used to construct these models needs to be evaluated. To this aim, we segmented 54 (n = 54; age = 34 ± 7 years; BMI = 26 ± 4 kg/m2) hip joint models from subject-specific computed tomography (CT) images, and measured multiple anatomical parameters (axial alpha angle, radial alpha angle, femoral head–neck offset, femoral neck–shaft angle, medial proximal femoral angle, femoral torsion, acetabular version and centre–edge angle) from both the multiplanar images and the 3D models, to assess the intraobserver, interobserver and intermethod reliabilities. We implemented a method to ensure that anatomical characteristics from segmented models were representative of original CT data. Observations from both CT data and 3D models demonstrated strong to near-perfect intraobserver, interobserver and intermethod agreements (p < 0.01). Bland–Altman plots indicated a slight discrepancy when assessing the asymptomatic FAI population, where planar CT images possibly did not capture the full depth of the cam deformity and underestimated geometric parameters. We indicated possible discrepancies to expect when segmenting hip joint models for clinical evaluation and finite element modelling, notably when observing femoral head–neck offset.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".