The gastric conduction system in health and disease: a translational review
Bibliographic record
Abstract
Gastric peristalsis is critically dependent on an underlying electrical conduction system. Recent years have witnessed substantial progress in clarifying the operations of this system, including its pacemaking units, its cellular architecture, and slow-wave propagation patterns. Advanced techniques have been developed for assessing its functions at high spatiotemporal resolutions. This review synthesizes and evaluates this progress, with a focus on human and translational physiology. A current conception of the initiation and conduction of slow-wave activity in the human stomach is provided first, followed by a detailed discussion of its organization at the cellular and tissue level. Particular emphasis is then given to how gastric electrical disorders may contribute to disease states. Gastric dysfunction continues to grow in their prevalence and impact, and while gastric dysrhythmia is established as a clear and pervasive feature in several major gastric disorders, its role in explaining pathophysiology and informing therapy is still emerging. New insights from high-resolution gastric mapping are evaluated, together with historical data from electrogastrography, and the physiological relevance of emerging biomarkers from body surface mapping such as retrograde propagating slow waves. Knowledge gaps requiring further physiological research are highlighted.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".