Effect of antiobesity medications in patients with type 2 diabetes mellitus
Bibliographic record
Abstract
Obesity is considered as a major health problem, as its prevalence continuously rises worldwide. One of the common health consequences of obesity is type 2 diabetes mellitus. Therefore, antiobesity management is a prerequisite in treating diabetic patients. Lifestyle modifications combined with pharmacological agents appear to be an effective approach. Sibutramine is a serotonin-noradrenaline reuptake inhibitor, which acts centrally by promoting the feeling of satiety and decreasing caloric intake, thus resulting in weight loss. A potential association with cardiovascular side effects has been noted. Orlistat, a gastric and pancreatic lipase inhibitor, also achieves significant weight loss and improves glycaemic status, but it has gastrointestinal side effects. Rimonabant, the first endocannabinoid CB1 antagonist, is associated with weight reduction and it improves diabetic parameters; nevertheless, it is associated with psychiatric disorders; indeed, a recently conducted safety review led to the temporal suspension of its commercialization. The above-mentioned medications seem to be currently useful agents for treating obesity in patients with type 2 diabetes mellitus. Other medications used for diabetes management, such as exenatide, liraglutide and pramlintide, have also shown body weight reduction. Ongoing research is needed to scrutinize the precise impact of these agents in the daily clinical practice of management of obesity in patients with type 2 diabetes mellitus.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".