Relationship Between Intragastric Meal Distribution, Gastric Emptying, and Gastric Neuromuscular Dysfunction in Chronic Gastroduodenal Disorders
Bibliographic record
Abstract
BACKGROUND: Chronic gastroduodenal symptoms arise from heterogeneous gastric motor dysfunctions. This study applied multimodal physiological testing using gastric emptying scintigraphy (GES) with intragastric meal distribution (IMD) and Gastric Alimetry body surface gastric mapping (BSGM) to define motility and symptom associations. METHODS: Patients with chronic gastroduodenal symptoms underwent simultaneous supine GES and BSGM with a 30 m baseline, 99mTC-labeled egg meal, and 4 h postprandial recording. IMD (ratio of counts in the proximal half of the stomach to the total gastric counts) was calculated immediately after the meal (IMD0), with < 0.568 defining abnormal IMD. BSGM phenotyping followed a consensus approach, based on normative spectral reference intervals. RESULTS: ), median IMD0 was 0.76 (IQR: 0.69-0.86) with 5 (7.5%) meeting abnormal IMD criteria. Delayed gastric emptying (n = 18) was associated with higher IMD0 (median 0.9 vs. 0.7, p = 0.004). On BSGM, 15 patients had abnormal spectrograms (5 [7.5%] high frequency and 10 [14.9%] low rhythm stability and/or amplitude); and in these patients, higher IMD0 (proximal retention) strongly correlated to delayed BSGM meal responses (R = -0.71, p = 0.003). Lower IMD, indicating antral distribution, correlated with higher gastric frequencies (R = -0.27, p = 0.03). BSGM abnormalities paired with abnormal IMD were associated with worse dyspeptic symptoms. CONCLUSION: Proximal retention of food as assessed by IMD correlated with delayed emptying, and in the presence of neuromuscular spectral abnormalities (abnormal frequencies or rhythms), delayed motility responses on BSGM. Patients with multiple motor abnormalities experience worse dyspeptic symptoms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".