Effect of Electroacupuncture at "Zusanli"(ST 36) on Esophageal Peristalsis in Cats
Bibliographic record
Abstract
Objective:To observe the impact of Zusanli point (ST 36) electro-acupuncture (EA) on cats' esophageal motility,and to discuss the relationship between Zusanli point (ST 36) and esophageal motility regulation. Methods:After endoscopic examination,28 cats were confirmed as healthy ones without esophagitis. All experimental cats were randomly divided into two groups:the Zusanli point (ST 36) group and the non-channels non-collaterals points group. The intraesophageal pressure and esophageal peristaltic wave were recorded by applying -7-1-1-1-2-5-5-5 type 8 Channel Fine Infusion Piezometric Tube (Canada) and UPS-2020 type Esophageal Pressure Measurement System (Netherlands). The following dynamic factors of esophagus were measured and recorded:lower esophageal peristalsis wave pressure (LEPP),upper esophageal peristalsis wave pressure (UEPP),peristalsis wave velocity. Results:There was no significant difference in esophageal peristalsis wave pressure and velocity between the Zusanli point (ST 36) group and the non-channels non-collaterals points group before EA. After EA,the lower esophageal peristalsis wave pressure in the Zusanli point (ST 36) group was increased from 75.64±19.81 mmHg to 88.93±23.29 mmHg (P0.05),and lower esophageal peristalsis wave velocity increased from 1.71±0.46 cm/s to 3.64±1.65 cm/s (P0.05). There were no statistically significant changes in upper esophageal peristalsis wave pressure and velocity after EA. In the non-channels non-collaterals points group,there were no statistically significant changes in esophageal peristaltic wave amplitude and conduction velocity. Conclusion:EA on Zusanli point (ST 36) of health cats might cause obvious increases in lower esophageal peristalsis wave pressure and velocity.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".