Volume rendering of three-dimensional fluoroscopic images for percutaneous scaphoid fixation: An in vitro study
Bibliographic record
Abstract
Percutaneous fixation of scaphoid fractures offers potential advantages to cast treatment but can be difficult to perform with conventional two-dimensional imaging. This study aimed to evaluate the use of a novel navigation technique using volume-rendered images derived from intraoperative cone-beam computed tomography imaging, without the need for typical patient-based registration. Randomized in vitro trials in which a guidewire was inserted into a scaphoid model were conducted to compare volumetric navigation to conventional fluoroscopic C-arm (n = 24). Central wire placement, surface breach, procedure time, drilling attempts, and radiation exposure were compared between groups. Compared to conventional percutaneous insertion, navigation achieved equal or significantly better placement of the guidewire with fewer drilling attempts and less radiation exposure. On average, navigation took 74 s longer to perform than the conventional method, which was statistically significant but clinically irrelevant. This evaluation suggests that the technology is promising and may have many clinical benefits including improved fixation placement, fewer complications, and less radiation exposure. The intraoperative workflow is more efficient and eliminates the need for preoperative computed tomography, image segmentation, and patient-based registration typical of traditional navigated procedures.
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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 | 0.003 |
| 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 | 0.001 | 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".