Magnetically Guided Catheters, Micro- and Nanorobots for Spinal Cord Stimulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it