Body Surface Gastric Mapping Delineates Specific Patient Phenotypes in Adolescents With Functional Dyspepsia and Gastroparesis
Bibliographic record
Abstract
BACKGROUND: Diagnosing pediatric patients with chronic gastroduodenal symptoms is clinically challenging, with the role of gastric emptying testing being controversial. Body Surface Gastric Mapping (BSGM) is a new diagnostic test that can identify specific patient phenotypes in adults with gastric dysfunction. This study evaluates whether BSGM can delineate specific phenotypes in adolescents and provide clinically meaningful distinctions between gastroparesis and functional dyspepsia diagnoses. METHODS: A prospective cross-sectional study recruited adolescents aged 12 to 21 between 2022 and 2024. Controls were recruited from New Zealand and patients from the Children's Hospital of Philadelphia, USA. BSGM followed a standardized protocol, including simultaneous symptom reporting and completion of validated symptom, psychometric, and physical health questionnaires. KEY RESULTS: Fifty-six subjects were recruited (31 controls, 25 patients); median age 16; 96% of patients were female. Control data showed that adult reference intervals provided an acceptable interpretation framework. Patients with FD (n = 10) and gastroparesis (n = 15) had common symptoms, mental health, quality of life, and functional disability (all p > 0.05). Three distinct BSGM phenotypes were identified: BSGM Normal (n = 10), BSGM Delay (n = 8), and Low Stability/Low Amplitude (n = 7), having spectral differences in BMI-Adjusted Amplitude 34.6 versus 39.1 versus 19.9 (p = 0.01) and Gastric Alimetry Rhythm Index: 0.45 versus 0.45 versus 0.19 (p = 0.003). BSGM phenotypes demonstrated differences in symptoms (nausea p = 0.04), physical health (p = 0.04), and psychometrics (anxiety p = 0.03). CONCLUSION AND INFERENCES: Adolescents with FD and gastroparesis have overlapping clinical profiles, making treatment challenging. Conversely, employing BSGM to categorize patients into distinct phenotypes reveals clinically relevant differences, offering avenues for individualized therapeutic pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".