Pharmacological and Surgical Intervention for the Prevention of Diabetes
Bibliographic record
Abstract
The increasing prevalence of diabetes is reaching epidemic proportion worldwide. Because of the associated morbidity and mortality, it is exerting major pressure on the healthcare system. With a better understanding of the pathophysiology of type-2 diabetes, the concept of primary prevention has emerged. A number of studies have confirmed that intensive lifestyle modification was very effective in the prevention of diabetes in the impaired glucose tolerance (IGT) population. However, maintaining long-term lifestyle modification is a major challenge. It is, therefore, important to have other strategies, either pharmacological or surgical, that can be used as an adjunct or alternative to lifestyle modification. The Chinese study showed that metformin and acarbose could reduce the risk of diabetes by 65 and 83%, respectively, in IGT subjects. The efficacy of metformin was confirmed by the Diabetes Prevention Program (31% risk reduction) and that of acarbose by the STOP-NIDDM trial (36% risk reduction) in a similar high-risk population. The TRIPOD study showed that troglitazone could reduce the risk of diabetes by 55% in Hispanic women with a history of gestational diabetes. And more recently, the XENDOS study showed that orlistat could reduced the risk of diabetes by 37% in obese subjects when used as an adjunct to an intensive lifestyle program. Three studies have suggested that bariatric surgery in morbidly obese subjects could reduce the risk of diabetes to near zero. Furthermore, a number of studies have examined the effect of a renin angiotensin aldosterone system inhibitor, as well as statin and hormone replacement therapy on the prevention of type-2 diabetes in high-risk subjects as secondary outcomes and have suggested that they could be of potential benefit. The accumulating evidence is now overwhelming. Yes, diabetes can be prevented or delayed in high-risk populations. With this new information, we need to design new strategies to screen high-risk populations and to implement the new treatments that have proven effective in the prevention of 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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".