A Steerable Neuroendoscopic Instrument Using Compliant Contact-Aided Joints and Monolithic Articulation
Bibliographic record
Abstract
Abstract This work presents the design of an articulate neuroendoscopic instrument (ANI), a handheld tool for use in minimally invasive neurosurgery. The instrument consists of a handle and a steerable tube-shaft with a distal end-effector. The design aims to increase the reach of surgeons operating through narrow channels within the center of the brain when approaching multiple targets from a single incision point. The steerable tube-shaft consists of a 1.6- mm notch-tube compliant joint mechanism augmented with contact-aids modeled after a gear geometry. The contact-aid geometry aims to address the performance tradeoff between stiffness, range-of-motion (RoM), and joint compactness for millimeter-scale notched-tube joints; it increases blocking force without sacrificing RoM. Finite element modeling (FEM) was used to refine design features, and the joint stiffness and RoM are assessed experimentally for three prototypes. The joint is incorporated into a tube-shaft instrument, and the assembled tool's stiffness properties are characterized. The prototype was then assessed in a validated neurosurgical simulator. An individual 1.24-mm outer-diameter notch-tube compliant joint with gear contact-aids is capable of 30 deg maximum bending and can sustain a 0.55 N blocking force with 0.5 mm displacement. A functional instrument shaft with a 15.5- mm-long articulating section was constructed from three joints in series with an external flexible stainless-steel sheath. It achieves a 6.7-mm bending radius at 75 deg maximum bending angle. In preclinical testing with an endoscopic third ventriculostomy and endoscopic tumor biopsy (ETV-ETB) simulator, the tool successfully completes the biopsy and fenestration maneuvers from a single burr-hole entry point. The ANI prototype uses contact-aid geometry incorporated into a compliant nitinol notched-tube joint to produce an articulate biopsy instrument for minimally invasive neurosurgical applications.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".