Parametric Study of Neural Gastric Electrical Stimulation in Acute Canine Models
Bibliographic record
Abstract
Manipulation of gastric motility by gastric electrical stimulation (GES) has been suggested as a minimally invasive alternative treatment of gastric motility disorders and obesity. However, only neural GES (NGES) has been successful in invoking gastric contractions. Nevertheless, the relationship between these contractions and the controlling NGES parameters has not been quantified. We aimed at determining the relationship between the electrical energy delivered to the tissue as a function of NGES parameters, and the strength and duration of the resulting invoked gastric contractions. Five healthy mongrel dogs underwent subserosal prepyloric implantation of two NGES electrode pairs. Gastric motility was captured by a force transducer implanted in the vicinity of the distal pair of stimulating electrodes. Custom-designed implantable stimulator delivered NGES with 8-16 V (peak-to-peak) amplitudes, and 60-100% duty cycles. Normalized motility index (MI) was utilized to quantify the contractions recorded from the force transducer. The MI increased with increasing voltage amplitudes. However, it remained remarkably constant across all duty cycles when voltage was held constant. Calculated motility generation efficiency indices (MGEI) indicated that highest energy efficiency for invoked motility was achieved at the lowest duty cycle. The parametric data obtained in the present study can be utilized to optimize the power efficiency of implantable gastric neurostimulators.
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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 | 0.001 |
| 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".