Long‐term Measurement of Gastric Motility using Passive Telemetry and Effect of Guar and Cellulose as Food Additives in Dogs
Bibliographic record
Abstract
The suitability of passive telemetry for long-term measurements of gastric motility in two groups of dogs with different body weights, four Beagles and four Labrador Retrievers, was investigated. An intra-abdominal measuring device, with a pressure sensor and electrodes within the gastric wall, allowed the continuous recording of the intensity and frequency of contractions simultaneously with an electromyogram (EMG). In fasting dogs a typical inter-digestive motility cycle was reproducible. Within 15 min of feeding the integral of the pressure curve increased significantly, reaching its maximum 30-45 min post-prandially. The peak frequency also significantly increased immediately after feeding, reaching the maximum of 22 contractions per 5 min. The post-prandial motility patterns of the groups were significantly different. The pressure amplitudes of the Labradors were significantly higher and the peak frequencies significantly lower than the Beagles. The addition of guar to the food (2.5% or 5%) leads to a significant reduction of the intensity of the antral contractions, whereas the frequency was hardly affected. In comparison, the effect of cellulose, as a food additive (2.5% or 5%), was rather modest. The intensity of the post-prandial contractions, influenced by cellulose, was significantly increased in Beagles, but was decreased in Labradors. Passive telemetry has been proven to be a suitable method for the long-term investigation of the physiological gastric motility and the effect of food additives. The measuring device was still functional after removal 8 weeks later.
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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) | 0.001 | 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 | 0.000 |
| 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 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".