3D atlas-based registration can calculate malalignment of femoral shaft fractures in six degrees of freedom
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: This study presents and evaluates a semi-automated algorithm for quantifying malalignment in complex femoral shaft fractures from a single intraoperative cone-beam CT (CBCT) image of the fractured limb. METHODS: CBCT images were acquired of complex comminuted diaphyseal fractures created in 9 cadaveric femora (27 cases). Scans were segmented using intensity-based thresholding, yielding image stacks of the proximal, distal and comminuted bone. Semi-deformable and rigid affine registrations to an intact femur atlas (synthetic or cadaveric-based) were performed to transform the distal fragment to its neutral alignment. Leg length was calculated from the volume of bone within the comminution fragment. The transformations were compared to the physical input malalignments. RESULTS: Using the synthetic atlas, translations were within 1.71 ± 1.08 mm (medial/lateral) and 2.24 ± 2.11 mm (anterior/posterior). The varus/valgus, flexion/extension and periaxial rotation errors were 3.45 ± 2.6°, 1.86 ± 1.5° and 3.4 ± 2.0°, respectively. The cadaveric-based atlas yielded similar results in medial/lateral and anterior/posterior translation (1.73 ± 1.28 mm and 2.15 ± 2.13 mm, respectively). Varus/valgus, flexion/extension and periaxial rotation errors were 2.3 ± 1.3°, 2.0 ± 1.6° and 3.4 ± 2.0°, respectively. Leg length errors were 1.41 ± 1.01 mm (synthetic) and 1.26 ± 0.94 mm (cadaveric). The cadaveric model demonstrated a small improvement in flexion/extension and the synthetic atlas performed slightly faster (6 min 24 s ± 50 s versus 8 min 42 s ± 2 min 25 s). CONCLUSIONS: This atlas-based algorithm quantified malalignment in complex femoral shaft fractures within clinical tolerances from a single CBCT image of the fractured limb.
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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) | 0.001 | 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