Intraoperative cone‐beam CT for correction of periaxial malrotation of the femoral shaft: A surface‐matching approach
Bibliographic record
Abstract
Limb length, alignment and rotation can be difficult to determine in femoral shaft fractures. Shaft axis rotation is particularly difficult to assess intraoperatively. Femoral malpositioning can cause deformity, pain and secondary degenerative joint damage. The aim of this study is to develop an intraoperative method based on cone-beam computed tomography (CBCT) to guide alignment of femoral shaft fractures. We hypothesize that bone surface matching can predict malrotation even with severe comminution. A cadaveric femur was imaged at 16 femoral periaxial malrotations (-51.2 degrees to 60.1 degrees). The images were processed resulting in an unwrapped bone surface plot consisting of a pattern of ridges and valleys. Fracture gaps were simulated by removing midline CT slices. The gaps were reconstituted by extrapolating the existing proximal and distal fragments to the midline of the fracture. The two bone surfaces were then shifted to align bony features. Periaxial malrotation was accurately assessed using surface matching (r2 = 0.99, slope 1.0). The largest mean error was 2.20 degrees and the average difference between repeated measurements was 0.49 degrees. CBCT can provide intraoperative high-resolution images with a large field of view. This quality of imaging enables surface matching algorithms to be utilized even with large areas of comminution.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".