Effects of valsartan compared to amlodipine on preventing type 2 diabetes in high-risk hypertensive patients: the VALUE trial
Bibliographic record
Abstract
CONTEXT: Type 2 diabetes is emerging as a major health problem, which tends to cluster with hypertension in individuals at high risk of cardiovascular disease. OBJECTIVE: To test for the first time the hypothesis that treatment of hypertensive patients at high cardiovascular risk with the angiotensin-receptor blocker (ARB) valsartan prevents new-onset type 2 diabetes compared with the metabolically neutral calcium-channel antagonist (CCA) amlodipine. DESIGN: Pre-specified analysis in the VALUE trial. Follow-up averaged 4.2 years. The risk of developing new diabetes was calculated as an odds ratio (OR) with 95% confidence intervals (CI) for different definitions of diabetes. PATIENTS: A sample of 9995 high-risk, non-diabetic hypertensive patients. INTERVENTIONS: Valsartan or amlodipine with or without add-on medication [hydrochlorothiazide (HCTZ) and other add-ons, excluding other ARBs, angiotensin-converting enzyme (ACE) inhibitors, CCAs]. MAIN OUTCOME MEASURE: New diabetes defined as an adverse event, new blood-glucose-lowering drugs and/or fasting glucose > 7.0 mmol/l. RESULTS: New diabetes was reported in 580 (11.5%) patients on valsartan and in 718 (14.5%) patients on amlodipine (OR 0.77, 95% CI 0.69-0.87, P < 0.0001). Using stricter criteria (without adverse event reports) new diabetes was detected in 495 (9.8%) patients on valsartan and in 586 (11.8%) on amlodipine (OR 0.82, 95% CI 0.72-0.93, P = 0.0015). CONCLUSION: Compared with amlodipine, valsartan reduces the risk of developing diabetes mellitus in high-risk hypertensive patients.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".