Effets de différents types de chirurgie bariatrique sur les concentrations circulantes de ghréline
Bibliographic record
Abstract
Obesity is a major risk factor of morbidity and mortality in human. Excessive accumulation of fat is intimately associated with the development of metabolic dysfunctions such as insulin resistance, type 2 diabetes, cardiovascular diseases, non-alcoholic fatty liver disease as well as certain types of cancers. Bariatric surgery is a necessary alternative for an increasing number of patients suffering from severe obesity with or without complications. Different types of interventions proved their efficiency in reducing excessive weight and improving metabolic function in obese patients. However, the mechanisms underlying these improvements remain largely uncharacterized. The neuroendocrine relevance of gastrointestinal (GI) peptides in the regulation of metabolic function has been abundantly reported in the literature. For instance, ghrelin, a GI hormone mainly derived from the stomach, was shown to exert both orexigenic and adipogenic effects in cellular, animal and clinical studies. The effects of specific bariatric surgery procedures on circulating ghrelin levels remain highly controversial. The present review first intends to classify published data in function of the type of bariatric intervention, patient types and conditions as well as the post-operative period at which ghrelin levels were measured. This allows proposing important factors that are likely to influence conclusions regarding the potential role of ghrelin as a mediator of metabolic improvements in obese patients who undergo bariatric 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".