Implantable, Transcutaneously Powered Neurostimulator System to Restore Gastrointestinal Motility
Bibliographic record
Abstract
This neurostimulator design addresses a deficiency in a promising new method for imposing a pre-designed and electrically-invoked motility patterns in the gastrointestinal (Gl) tract. Sequential Neural Gastrointestinal Stimulation (SNGES) has been shown to be effective in artificially restoring the motility of the stomach and the colon. However, the availability of an implantable device to implement this innovative stimulation protocol has been lacking. The work presented in this paper was aimed at designing a transcutaneously powered implantable device to facilitate SNGES testing. Discrete electrical components and a Complex Programmable Logic Device (CPLD) were used to rapidly prototype the neurostimulator. Transcutaneous Energy Transfer (TET) powered the device to eliminate the need for an implantable battery. This research resulted in the design, implementation, and testing of an innovative transcutaneously powered device that is small and safe enough to be chronically implanted in experimental animals or humans. The device conforms to the stimulation algorithm used in the SNGES method for restoring GI motility. A detailed design procedure addresses key issues such as charge balance, tissue heating, and biocompatibility. This design procedure lays the foundation for the future architecture of a mixed-signal ASIC based neurostimulator that would lead to further minimization of power consumption and size.
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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".