Modification of gastric pH in the fasted dog
Bibliographic record
Abstract
OBJECTIVES: The aim was to compare the ability of pretreatments to consistently adjust gastric conditions to low or high pH in the fasted state in dogs. METHODS: Four male Labrador/Labrador-cross dogs weighing 25-35 kg were surgically equipped with a ventricle fistula cannula in the stomach and a jejunal nipple valve stoma. Dogs were fasted overnight before the experiments, with free access to water. The pH in the dogs' stomach was modified either orally with buffers (0.1 mol/l HCl-KCl, 0.05 mol/l glycine-HCl, 0.1 mol/l citrate or 0.1 mol/l BIS-TRIS) or intravenously with pharmacological agents (pentagastrin 4-6 microg/kg, ranitidine 50 mg or omeprazole 1 mg/kg). Intragastric pH was recorded continuously for 2 h with an electrode connected to an ambulatory pH meter. Chyme was collected simultaneously from the jejunal stoma as an approximate measure of gastric emptying. KEY FINDINGS: 0.1 mol/l HCl-KCl buffer p.o. and 1 mg/kg omeprazole i.v. attained low and high gastric pH more reproducibly (11/11 and 6/7 experiments met target values of pH < 3 and > 4, respectively) and for a longer duration (average time exceeding target value 90 and 103 min, respectively) than the other buffers and pharmacological pretreatments. The starting pH did not alter the modifiers' capacity to increase or decrease the pH. However, the lag time before chyme appeared at the jejunal stoma appeared to be longer when the pH was low and shorter when the pH was high. CONCLUSIONS: To achieve a consistently low gastric pH in fasting dogs, 0.1 mol/l HCl-KCl buffer should be administered orally, 15 min before the dosage form. To elevate the gastric pH reproducibly, omeprazole 1 mg/kg should be administered intravenously at least 90 min before oral administration of the dosage form.
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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 | 0.001 |
| 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".