A statistical shape model of femoral head-neck cross sections using principal tangent components
Bibliographic record
Abstract
Diagnosis of orthopedic conditions, such as femoroacetabular impingement, is difficult to automate. Current methods rely on human analysis of contours that are derived from planar cross-sections of volumetric data. We propose a statistical shape model for analyzing proximal femoral contours. Current frameworks, based on principal component analysis, appear inadequate for analyzing femoral contours because of the complex deformations in diseased patients. We present an analysis based on principal tangent components as a new method for shape description. This model represents deformations as a flow on a manifold, then performs calculations on the associated tangent spaces through exponential mapping, which is appealing because computations on the tangent spaces are Euclidean even if the actual deformations are highly nonlinear. The new model recovered 98% of the contour shapes using only two components, whereas the conventional method needed 48 components to achieve the same reconstruction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".