3D reconstruction of the proximal femur with low-dose digital stereoradiography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Accurate three-dimensional (3D) geometry of the proximal femur may be helpful for fracture risk evaluation, as well as for planning and assisting surgical procedures. The purpose of this study was to apply and validate a stereoradiographic 3D reconstruction method on the proximal femur from radiographic contours identified on bi-planar radiographs. MATERIALS AND METHODS: Twenty-five excised non-pathologic proximal femurs were investigated using a low-dose digital radiographic device. Three-dimensional personalized models were reconstructed using the Non-Stereo Corresponding Contours (NSCC) algorithm. Three-dimensional CT-scan reconstructions were defined as geometric references for the comparison protocol, in order to assess the accuracy and reproducibility of the personalized 3D stereoradiographic reconstructions. In addition, the reliability of a set of 3D parameters obtained from stereoradiographic models was evaluated. RESULTS: This study demonstrated the validity of the NSCC method when applied to the proximal femur, with good results for accuracy (mean error = 0.7 mm) and reproducibility (Wilcoxon test: p > 0.28). Moreover, a precision study for the set of 3D parameters yielded a coefficient of variation lower than 5%. CONCLUSIONS: Once this approach has been validated in vivo, it should find multiple applications in therapeutic fields (e.g., for surgical planning, computer assisted surgery, etc.), as well as in diagnostic contexts (e.g., equilibrium studies or osteoporosis fracture risk assessment).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it