Meal effects on gastric bioelectrical activity utilizing body surface gastric mapping in healthy subjects
Bibliographic record
Abstract
BACKGROUND: Gastric sensorimotor disorders are prevalent. While gastric emptying measurements are commonly used, they may not fully capture the underlying pathophysiology. Body surface gastric mapping (BSGM) recently emerged to assess gastric sensorimotor dysfunction. This study assessed varying meal size on BSGM responses to inform test use in a wider variety of contexts. METHODS: Data from multiple healthy cohorts receiving BSGM were pooled, using four different test meals. A standard BSGM protocol was employed: 30-min fasting, 4-h post-prandial, using Gastric Alimetry® (Alimetry, New Zealand). Meals comprised: (i) nutrient drink + oatmeal bar (482 kcal; 'standard meal'); (ii) oatmeal bar alone; egg and toast meal, and pancake (all ~250 kcal). Gastric Alimetry metrics included BMI-adjusted Amplitude, Principal Gastric Frequency, Gastric Alimetry Rhythm Index (GA-RI) and Fed:Fasted Amplitude Ratio (ff-AR). KEY RESULTS: 238 participants (59.2% female) were included. All meals significantly increased amplitude and frequency during the first postprandial hour (p < 0.05). There were no differences in postprandial frequency across meals (p > 0.05). The amplitude and GA-RI of the standard meal (n = 110) were significantly higher than the energy bar alone (n = 45) and egg meal (n = 65) (all p < 0.05). All BSGM metrics were comparable across the three smaller meals (p > 0.05). A higher symptom burden was found in the oatmeal bar group versus the standard meal and pancake meal (p = 0.01, 0.003, respectively). CONCLUSIONS & INFERENCES: The consumption of lower calorie meals elicited different postprandial responses, when compared to the standard Gastric Alimetry meal. These data will guide interpretations of BSGM when applied with lower calorie meals.
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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) | 0.002 | 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".