Magnetically Guided Catheters, Micro- and Nanorobots for Spinal Cord Stimulation
Bibliographic record
Abstract
Spinal cord stimulation (SCS) is an established treatment for refractory pain syndromes and has recently been applied to improve locomotion. Several technical challenges are faced by surgeons during SCS lead implantation, particularly in the confined dorsal epidural spaces in patients with spinal degenerative disease, scarring and while targeting challenging structures such as the dorsal root ganglion. Magnetic navigation systems (MNS) represent a novel technology that uses externally placed magnets to precisely steer tethered and untethered devices. This innovation offers several benefits for SCS electrode placement, including enhanced navigation control during tip placement, and the ability to position and reposition the lead in an outpatient setting. Here, we describe the challenges of SCS implant surgery and how MNS can be used to overcome these hurdles. In addition to tethered electrode steering, we discuss the navigation of untethered micro- and nanorobots for wireless and remote neuromodulation. The use of these small-scale devices can potentially change the current standard of practice by omitting the need for electrode and pulse generator implantation or replacement. Open questions include whether small-scale robots can generate an electrical field sufficient to activate neuronal tissue, as well as testing precise navigation, placement, anchoring, and biodegradation of micro- and nanorobots in the in vivo environment.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".