Bibliographic record
Abstract
The gastrointestinal tract is an often overlooked but complex endocrine organ, home to dozens of regulatory peptides produced in specialized endocrine cells and enteric neurons. These hormones subserve complex roles as signals regulating appetite, gastrointestinal motility, control of secretion from the exocrine and endocrine pancreas, and nutrient absorption (1). The majority of gut peptides are secreted within minutes of nutrient ingestion and rise transiently in the circulation, with levels rapidly falling back to basal levels after termination of feeding. Because complex disorders such as obesity and diabetes involve imbalances in the control of energy ingestion and disposal, there is considerable interest in understanding the physiological role and therapeutic potential of gut peptides in the control of nutrient assimilation. Two enteroendocrine-derived peptides, glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1), play important roles in preparing the pancreas to handle an incoming nutrient load. Both GIP and GLP-1 function as incretin hormones, gut-derived peptides that potentiate insulin secretion from the islet β-cell in a glucose-dependent manner (2, 3). Considerable recent evidence suggests that incretin-based therapies may be useful for the treatment of type 2 diabetes because continuous administration of GLP-1 produces substantial improvements in glucose control and β-cell function in subjects with type 2 diabetes (4). However, the rapid degradation of both GIP and GLP-1 by the aminopeptidase, dipeptidyl peptidase-4 (DPP-4), has fostered the development of degradation-resistant GLP-1R agonists such as exendin-4 (Exenatide), now employed as a twice daily injectable agent for the treatment of type 2 diabetes (5). Complementary efforts to prolong incretin action include the use of chemical inhibitors of DPP-4 activity, and several DPP-4 inhibitors have completed extensive clinical testing in subjects with type 2 diabetes.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.039 | 0.041 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".