Gastric Alimetry Expands Patient Phenotyping in Gastroduodenal Disorders Compared with Gastric Emptying Scintigraphy
Bibliographic record
Abstract
INTRODUCTION: Gastric emptying testing (GET) assesses gastric motility, however, is nonspecific and insensitive for neuromuscular disorders. Gastric Alimetry (GA) is a new medical device combining noninvasive gastric electrophysiological mapping and validated symptom profiling. This study assessed patient-specific phenotyping using GA compared with GET. METHODS: Patients with chronic gastroduodenal symptoms underwent simultaneous GET and GA, comprising a 30-minute baseline, 99m TC-labelled egg meal, and 4-hour postprandial recording. Results were referenced to normative ranges. Symptoms were profiled in the validated GA App and phenotyped using rule-based criteria based on their relationships to the meal and gastric activity: (i) sensorimotor, (ii) continuous, and (iii) other. RESULTS: Seventy-five patients were assessed, 77% female. Motility abnormality detection rates were as follows: GET 22.7% (14 delayed, 3 rapid), GA spectral analysis 33.3% (14 low rhythm stability/low amplitude, 5 high amplitude, and 6 abnormal frequency), and combined yield 42.7%. In patients with normal spectral analysis, GA symptom phenotypes included sensorimotor 17% (where symptoms strongly paired with gastric amplitude, median r = 0.61), continuous 30%, and other 53%. GA phenotypes showed superior correlations with Gastroparesis Cardinal Symptom Index, Patient Assessment of Upper Gastrointestinal Symptom Severity Index, and anxiety scales, whereas Rome IV Criteria did not correlate with psychometric scores ( P > 0.05). Delayed emptying was not predictive of specific GA phenotypes. DISCUSSION: GA improves patient phenotyping in chronic gastroduodenal disorders in the presence and absence of motility abnormalities with increased correlation with symptoms and psychometrics compared with gastric emptying status and Rome IV criteria. These findings have implications for the diagnostic profiling and personalized management of gastroduodenal disorders.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".