The Impact of Bariatric Surgery in Patients with Type-2 Diabetes Mellitus
Bibliographic record
Abstract
Over 220 million individuals have type-2 diabetes mellitus (T2DM) worldwide. Obesity has been identified as a significant risk factor for the development of T2DM. Overweight or obese individuals develop insulin resistance with resultant hyperinsulinemia. This process may progress to impaired glucose intolerance and eventual T2DM. There is strong evidence indicating that bariatric surgery may produce sustainable long-term weight loss in obese individuals. Bariatric surgery consists of surgical operations classified as either primarily restrictive or malabsorptive. Restrictive bariatric procedures include gastric banding or sleeve gastrectomy, while malabsorptive procedures included gastric bypass and biliopancreatic diversion. Malabsorptive procedures have been shown to be superior in producing dramatic weight loss along with resolution or improvement of T2DM. Interestingly, improvement of diabetes has been shown to occur shortly following malabsorptive bariatric surgery, prior to significant weight loss, suggesting that hormone-mediated mechanisms may be involved. As the prevalence of obesity and T2DM continues to rise, so may the role of bariatric surgery to combat this growing epidemic. Keywords: Obesity, Type 2 diabetes mellitus, Bariatric surgery, Metabolic surgery, Gastric banding (LAGB), Sleeve gastrectomy (LSG), Roux-en-Y gastric bypass (RYGB), Biliopancreatic diversion (BPD), Body mass index (BMI), Insulin sensitivity
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".