S1851 Physiology-Guided, Mechanism-Based Digital Phenotyping of Gastroduodenal Symptoms
Bibliographic record
Abstract
Introduction: Current classification schemes for chronic gastroduodenal symptoms substantially overlap and therefore do not clearly guide patient-specific therapy. We hypothesised that a mechanism-based approach to symptom classification may offer a valid and more specific alternative scheme. Methods: We performed a multicenter, prospective cohort study of patients meeting Rome-IV criteria for functional dyspepsia (FD) and chronic nausea and vomiting syndromes (CNVS). Gastric Alimetry™ (Alimetry, New Zealand) was used for body surface gastric mapping spectral analysis and time-of-test symptom profiling. A standardized digital classification framework separated patients into those with a) abnormal spectral analyses (suspected neuromuscular pathologies); normal spectral analyses with b) symptoms correlated to gastric amplitude (sensorimotor, post-gastric, and activity-relieved); and c) symptoms independent of gastric amplitude (continuous, meal-relieved, meal-induced). Results: 210 patients (80% female, median age 37) of whom 169 met criteria for CNVS and 206 met criteria for FD (79% overlapping) were included. Overall, 83% with unexplained gastroduodenal symptoms were phenotyped, with 79/210 (37.6%) classified as having a spectral abnormality on gastric mapping. Of the remaining 131, 37 (17.6%) were classified as ‘continuous pattern’, 28 (13.3%) as ‘meal-induced pattern’, and 15 (7.1%) as ‘sensorimotor pattern’. When spectral analyses were normal, symptom patterns that were independent of gastric amplitude such as continuous, meal-relieved, and meal-induced patterns were more strongly correlated with depression and anxiety (PHQ-2: exp(β) 2.38, P = 0.024), STAI-SF score: exp(β) 1.21, P = 0.021). Conclusion: Standardized time-of-test symptom profiles offer a novel approach to classifying patients based on proposed mechanisms of disease. These groupings correlated with chronic symptoms, quality of life, and psychological factors, demonstrating initial clinical validity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".