A Novel Articulating Bone Cutting Tool for Minimally Invasive Craniosynostosis Surgery
Bibliographic record
Abstract
Abstract Craniosynostosis involves early fusion of cranial sutures, resulting in an abnormal head shape and functional issues such as elevated intracranial pressure. Treatment involves surgery to correct the head shape and to allow unrestricted brain growth. This work aims to overcome the restrictions of current instruments by developing a novel articulated bone cutting tool for minimally invasive craniosynostosis surgery. A handheld tendon-driven tool with a bending section was developed to enhance reachability along the skull. The tool comprises a driving unit, a bending section attached to an end-effector, and a flexible endoscope. The prototype was developed and characterized to validate its accuracy, stiffness, bone-cutting capability, and reachability. A prototype was developed with a high reachability of 72.2%, 71.6%, 78.4% (length of the osteotomy/total planned path) using sagittal, metopic, and unicoronal craniosynostosis skull models, respectively. The tool demonstrated low deflection (≤5 mm for all configurations except for 0 deg bending) under 5 N external force and is capable of cutting bone-like materials with varying bending angles (range from 45 deg to 90 deg). These results indicate the potential of the bone-cutting tool to provide a paradigm shift in the treatment of craniosynostosis, expanding the benefits of minimally invasive approach to more patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".