Emerging Piglet Models of Neonatal Short Bowel Syndrome
Bibliographic record
Abstract
Short bowel syndrome (SBS) is a growing problem in the human neonatal population. In infants, SBS is the leading cause of intestinal failure, the state of being unable to absorb sufficient nutrients for growth and development. Neonates with SBS are dependent on long-term parenteral nutrition therapy, but many succumb to the complications of sepsis and liver disease. Research in neonatal SBS is challenged by the ethical limits of studying sick human neonates and the heterogeneous nature of the disease process. Outcomes in SBS vary depending on residual intestinal anatomy, intestinal length, patient age, and exposure to nutrition therapies. The neonatal piglet serves as an appropriate translational model of the human neonate because of similarities in gastrointestinal ontogeny, physiological maturity, and adaptive processes. Re-creating the disease process in a piglet model presents a unique opportunity for researchers to discover novel insights and therapies in SBS. Emerging piglet models of neonatal SBS now represent the entire spectrum of disease seen in human infants. This review aims to contextualize these emerging piglet models within the context of SBS as a heterogeneous disease. We first explore the factors that account for SBS heterogeneity and then explore the suitability of the neonatal piglet as an appropriate translational animal model. We then examine differences between the emerging piglet models of neonatal SBS and how these differences affect their translational potential to human neonates with SBS.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".