Reduction of multi-fragment fractures of the distal radius using atlas-based 2D/3D registration
Bibliographic record
Abstract
We describe a method to guide the surgical fixation of distal radius fractures. The method registers the fracture fragments to a volumetric intensity-based statistical anatomical atlas of distal radius, reconstructed from human cadavers and patient data, using a few intra-operative X-ray fluoroscopy images of the fracture. No pre-operative Computed Tomography (CT) images are required, hence radiation exposure to patients is substantially reduced. Intra-operatively, each bone fragment is roughly segmented from the X-ray images by a surgeon, and a corresponding segmentation volume is created from the back-projections of the 2D segmentations. An optimization procedure positions each segmentation volume at the appropriate pose on the atlas, while simultaneously deforming the atlas such that the overlap of the 2D projection of the atlas with individual fragments in the segmented regions is maximized. Our simulation results shows that this method can accurately identify the pose of large fragments using only two X-ray views, but for small fragments, more than two X-rays may be needed. The method does not assume any prior knowledge about the shape of the bone and the number of fragments, thus it is also potentially suitable for the fixation of other types of multi-fragment fractures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".