Manipulation of food intake and weight dynamics using retrograde neural gastric electrical stimulation in a chronic canine model
Bibliographic record
Abstract
Neural gastric electrical stimulation (NGES) could be a new technique for treating obesity. However, chronic animal experimentation exploring the efficacy of this therapy is lacking. In this study we investigated the utility of retrograde NGES in a chronic canine model. Nine mongrel dogs (26.8 +/- 5.2 kg) underwent laparoscopic implantation of 2-channel neurostimulator leads in the distal antrum. Five dogs formed a control group and four dogs underwent stimulation. Food intake and weight dynamics were regularly monitored during two separate research protocols, each comprising 2-week baseline, stimulation and washout periods. The stimulation voltage was constant in the first protocol and was ramped in the second. In the first protocol three out of the four stimulated dogs demonstrated significant decrease in food intake (P < 0.05). However, this materialized in a significant weight reduction in one dog only. In the second protocol, all stimulated dogs exhibited significant food intake and weight reduction (P < 0.05) compared to controls. Necropsies and histopathological analysis did not reveal any abnormalities in the stomach, the adjacent organs or around the implant. NGES could be a safe new technique for reducing food intake and weight and, therefore, it might be helpful for treating obesity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".