A Biomechanics-Aware Robot-Assisted Steerable Drilling Framework for Minimally Invasive Spinal Fixation Procedures
Bibliographic record
Abstract
In this paper, we propose a novel biomechanics-aware robot-assisted steerable drilling framework with the goal of addressing common complications of spinal fixation procedures occurring due to the rigidity of drilling instruments and implants. This framework is composed of two main unique modules to design a robotic system including (i) a Patient-Specific Biomechanics-aware Trajectory Selection Module used to analyze the stress and strain distribution along an implanted pedicle screw in a generic drilling trajectory (linear and/or curved) and obtain an optimal trajectory; and (ii) a complementary semi-autonomous robotic drilling module that consists of a novel Concentric Tube Steerable Drilling Robot (CT-SDR) integrated with a seven degree-of-freedom robotic manipulator. This semi-autonomous robot-assisted steerable drilling system follows a multi-step drilling procedure to accurately and reliably execute the optimal hybrid drilling trajectory (HDT) obtained by the Trajectory Selection Module. Performance of the proposed framework has been thoroughly analyzed on simulated bone materials by drilling various trajectories obtained from the finite element-based Selection Module using Quantitative Computed Tomography (QCT) scans of a real patient's vertebra.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".