Using Ultrasound to Guide the Insertion of Pedicle Screws during Scoliosis Surgery
Bibliographic record
Abstract
Scoliosis surgery involves the insertion of screws and/or hooks into selected vertebrae to secure a pre-bent rod placed along the concave side of the spine. Usually conventional x-rays will be taken before the surgery to plan the alignment and positioning of the pedicle screws. However, reports state that perforation rate range from 6% to 54%. A misalignment of a pedicle screw can potentially cause permanent neurological spinal cord injury or even a life-threatening vascular injury. Because of the importance of positioning and aligning of pedicle screws, we are working on an ultrasound method to guide the insertion of pedicle screws in real time. A pulse-echo immersion experiment was set up to study how well the edges of cortical bone could be detected using a bovine spinous process in-vitro. Two ultrasound frequencies (3.5 MHz and 5.0 MHz) were considered in this study. This preliminary study shows that ultrasound is able to penetrate cortical bone and reflect back from the outer boundary. All interfaces are clearly identified for both frequencies. Strong reflection signals are obtained when the beam is normal to the interface. Derived thickness values from the reflections are comparable with those from micro-CT image. The 5.0 MHz ultrasound frequency provided better resolution than the 3.0 MHz frequency.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".