Bibliographic record
Abstract
The function of the enteric neuronal system is poorly understood.While an understanding of the morphology of the ENS has long been appreciated, very little is known regarding the physiological function of these pathways.Aside from Hirschprung's disease there are no common models to extrapolate a relationship between morphology and function.Recent experimental evidence suggests that the ENS is actively involved in the control of such diverse actions of the bowel as motility, secretion, regional blood flow, permeability, and nutrient transport capacity.Improved understanding of signalling pathways such as the role of the primary afferent neurons, which project from the mucosa to the submucosal and inter myenteric plexuses are being appreciated.These slow acting nerve fibres appear to integrate mucosal signals with intrinsic gut function, and project to the central nervous system.Within this model, our own work has focused on the effect of enteric hormones such as Glucagon Like Peptide 2, which appears to act through this system.This hormone activates the enteric neuronal system, and signals effects on motility, permeability, and nutrient transport capacity.Long term stimulation results in trophic effects.Ongoing work to map the neuronal pathways involved is underway.An improved understanding of these types of mechanisms may have far reaching implications for the surgical community, serving as a framework to understand the effects of early feeding on intestinal function and im munology.This paper will detail our current levels of understanding of these pathways, and explore how they may in future be manipulated to improve the care of our surgical patients.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".