Beyond the Operating Room: A Simulator for Sacroiliac Screw Insertion
Bibliographic record
Abstract
Current teaching techniques for orthopedic screw insertions involve "learning by doing" in the operating room. Minimally invasive insertion of sacroilliac (SI) screws is a relatively uncommon operation, providing scant opportunity for training outside of a few major centers. As such, SI screw insertion is a prime candidate for simulator-based training. This work describes the development and implementation of a simulator for minimally invasive SI screw insertion using accurate 3-dimensional (3D) computed tomography (CT)-based visualization of the pelvic and upper sacral anatomy. The simulator was designed in Tool Command Language atop the Amira 3D visualization package. CT images of pelvic regions were automatically segmented to generate 3D surfaces. Using inlet and outlet 3D views, guidewire insertion can be performed followed by an appropriately sized SI screw. The simulator was found to provide a realistic representation of the pelvis, and test users reported increased understanding of the procedure of SI screw insertion following use. The 3D reconstructions of the pelvis allowed for visual correlations between CT slices and inlet and outlet x-ray views. Pilot work with surgical trainees suggests the tool's value in increasing the familiarity of surgical trainees to visualize the pelvis in 3D and perform SI screw insertion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".