Gastrointestinal peptides after bariatric surgery and appetite control: are they in tuning?
Bibliographic record
Abstract
PURPOSE OF REVIEW: To discuss the contribution of gut peptides to the improved appetite control that results from obesity surgery. RECENT FINDINGS: The treatment options for morbid obesity are few, and bariatric interventions have become a common intervention to treat large excesses in adiposity. The mechanisms explaining the large weight losses and the notable long-term maintenance that characterize bariatric interventions have intrigued researchers for a few decades. One of these mechanisms may entail the altered secretion pattern of appetite-related gut peptides. In fact, an increasing number of studies have highlighted the exaggerated nutrient-stimulated response of some of these anorectic hormones, namely peptide YY and glucagon-like peptide-1, along with a down-regulation of ghrelin, the only orexigenic hormone known in humans. Among most recent findings, a suboptimal gut peptide response was reported in poor responders to bypass surgery. In summary, results currently available have brought us closer to understanding the link between the altered gut peptide secretion and the improved appetite control resulting from obesity surgery. SUMMARY: The surge of literature related to the exaggerated nutrient-stimulated response of gut peptides after bypass intervention provides increasing support for the role of some of these hormones in the long-term success rates of obesity surgery.
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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) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".