Tetherless Mobile Micro-Surgical Scissors Using Magnetic Actuation
Bibliographic record
Abstract
Current minimally-invasive surgical tools suffer from lack of scalability and restricted access to some surgical sites using a laparoscopic probe. This paper introduces a proof-of-concept prototype of the first completely wireless surgical scissors capable of dexterous motion and cutting in a remote environment as a mobile microrobotic device. The 15 mm untethered surgical scissors are custom made from sharpened titanium sheets with a magnet on each blade for actuating force and control. A super-elastic nitinol wire acts as a restoring spring and results in a simple design with no pin joint which is difficult to fabricate at small sizes. To actuate and control the scissors, a 3D magnetic coil system is used here for testing and demonstration. An external magnetic flux density of 20 mT can be generated using the coils and is used for cutting as well as orienting, moving and closing the scissors. In this first prototype setup, the scissors can generate up to 75 mN of cutting force, and we demonstrate the cutting of agar. As a proof of concept demonstration of the potential use of the scissors as a completely untethered surgical tool, we robotically maneuver the scissors to a target location in a confined environment where they cut through agar and return to their initial position.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".