Bibliographic record
Abstract
The growing prevalence of Type 2 diabetes with its high morbidity and excess mortality is imposing a heavy burden on healthcare systems. Because of the magnitude of the problem, obviating diabetes has been a long-standing dream. In the last decade, a number of intervention strategies have been shown to be effective for the prevention of diabetes in high-risk populations with prediabetes. Seven studies have now confirmed that lifestyle modifications, including weight-reducing diets and exercise programs, are very effective in precluding or delaying Type 2 diabetes in high-risk populations with impaired glucose tolerance (IGT). Two major trials are the Diabetes Prevention Study (n = 522) from Finland and the Diabetes Prevention Program (n = 3234) from the US. Both studies have shown that intensive lifestyle intervention could reduce the progression of IGT to diabetes by 58%. Furthermore, four currently-available drugs have been established as being effective in preventing diabetes in subjects with prediabetes. The Diabetes Prevention Program revealed that metformin 850 mg b.i.d. reduced the risk of diabetes by 31%. The STOP-NIDDM (Study To Prevent Non-Insulin-Dependent Diabetes Mellitus) trial (n = 1429) showed that acarbose 100 mg t.i.d. with meals decreased the incidence of diabetes by 36% when the diagnosis was based on 2 oral glucose tolerance tests. The XENDOS (Xenical in the Prevention of Diabetes in Obese Subjects) study examined the use of orlistat, an antiobesity drug, as an adjunct to an intensive lifestyle modification program in obese non-diabetic subjects. Orlistat treatment resulted in a 37% decline in the development of diabetes. More recently, the DREAM (Diabetes Reduction Assessment with Ramipril and Rosiglitazone Medication) study (n = 5269) demonstrated that rosiglitazone at 8 mg once/day in subjects with prediabetes (IGT and/or impaired fasting glucose) was effective in reducing the risk of diabetes by 60%. It can be concluded that Type 2 diabetes can be prevented or delayed through lifestyle modifications and/or pharmacologic interventions. This is a fact.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.029 |
| Insufficient payload (model declined to judge) | 0.005 | 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".