Design of an ultrasound‐emitting drill guide for freehand pedicle screw navigation
Bibliographic record
Abstract
BACKGROUND: Accurate pedicle screw placement in spinal surgery is critical as inaccuracies can lead to morbidity and suboptimal outcomes. Navigation and robotics have reduced malplacement rates, but their adoption is limited by high costs, learning curves, surgical time, and radiation. The authors propose an ultrasound-emitting and self-localising drill guide for precise screw placement that overcomes the limitations of current techniques. MATERIALS AND METHODS: The preliminary configuration analysis involves systematically varying design parameters and assessing localization performance using lumbar spine MRI based simulations. The authors evaluate localization techniques based on accuracy and optimization capture range. RESULTS: Results suggest that feasible designs can accurately estimate position. A promising design features a 5 mm radius cannula with ten 35mm-long ultrasound strips, 32 elements per strip, and a fanned-out emission profile. A multi-start active-set optimization algorithm with six initial estimates ensures reliable and efficient localization. CONCLUSIONS: The simulation suggests that the proposed design can achieve sufficient localization accuracy for pedicle screw navigation. These findings will guide the fabrication of a novel ultrasound-emitting drill guide for further evaluation and physical testing.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".