Beyond the Operating Room: A Simulator for Sacroiliac Screw Insertion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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