Distinct subgroups in gastroparesis defined by simultaneous body surface gastric mapping and gastric emptying breath testing
Bibliographic record
Abstract
Abstract Background Gastroparesis is a heterogeneous disorder with several contributing pathophysiologies. In this study we used simultaneous body surface gastric mapping (BSGM) and gastric emptying breath testing (GEBT) to subgroup patients with gastroparesis based on dynamic spectral meal response profiles and emptying rate. Methods Patients with chronic gastroduodenal symptoms and negative gastroscopy underwent simultaneous BSGM and gastric emptying breath test (GEBT) with 30 minutes fasting and 4 hours postprandial recording. In addition to standard metrics, the BSGM ‘Meal Response Ratio’ (MRR) compared amplitude in the first 2 hours postprandially to the subsequent 2 hours (lagged meal response ≤1). Results 143 patients underwent simultaneous BSGM and GEBT (79% female, median age 31 years, median BMI 23 kg/m 2 ). Delayed emptying occurred in 25.2% (n = 36). Those with a lagged meal response had longer T 1/2 (median 95.0 [IQR 59-373] vs median 78.0 [IQR 31-288], p=0.009) and higher rates of delayed emptying (42.9% vs 16.7% p = 0.03). BSGM phenotypes identified in patients with delayed emptying were: lagged meal response (25%), low gastric amplitude / rhythm stability (30.6%), elevated gastric frequencies (11.1%), and normal BSGM spectral analysis (33.3%). T 1/2 weakly correlated with worse total symptom burden score (r = 0.18, p = 0.03). Conclusion Combined BSGM and gastric emptying testing defines subgroups of gastroparesis based on contributing disease mechanisms, including a novel group with delayed post-prandial onset of gastric motor activity. Improved patient phenotyping in gastroparesis may enable improved therapeutic targeting through these biomarkers of disease processes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".