Comparison of Gastric Alimetry <sup>®</sup> body surface gastric mapping versus electrogastrography spectral analysis
Bibliographic record
Abstract
Abstract Electrogastrography (EGG) non-invasively evaluates gastric motility but is viewed as lacking clinical utility. Gastric Alimetry® is a new diagnostic test that combines high-resolution body surface gastric mapping (BSGM) with validated symptom profiling, with the goal of overcoming EGG’s limitations. This study directly compared EGG and BSGM to define performance differences in spectral analysis. Comparisons between Gastric Alimetry BSGM and EGG were conducted by protocolized evaluation of 178 subjects (110 controls; 68 nausea and vomiting (NVS) and/or type 1 diabetes (T1D)). Comparisons followed standard methodologies for each test (pre-processing, post-processing, analysis), with statistical evaluations for group-level differences, symptom correlations, and patient-level classifications. BSGM showed substantially tighter frequency ranges vs EGG in controls. Both tests detected rhythm instability in NVS, but EGG showed opposite frequency effects in T1D. BSGM showed an 8x increase in the number of significant correlations with symptoms. BSGM accuracy for patient-level classification was 0.78 for patients v. controls and 0.96 as compared to blinded consensus panel; EGG accuracy was 0.54 and 0.43. EGG detected group-level differences in patients, but lacked symptom correlations and showed poor accuracy for patient-level classification, explaining EGG’s limited clinical utility. BSGM demonstrated substantial performance improvements across all domains.
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| 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".