Bibliographic record
Abstract
CONTEXT: Type 2 diabetes is a growing clinical and public health problem. Preventive efforts related to lifestyle modification are not always successful; therefore, alternative prevention strategies need to be studied. OBJECTIVE: To investigate the effectiveness of ramipril, an angiotensin-converting enzyme inhibitor, in preventing diabetes among high-risk persons. DESIGN, SETTING, AND PARTICIPANTS: The randomized, controlled Heart Outcomes Prevention Evaluation trial of 5720 patients older than 55 years without known diabetes but with vascular disease who were followed up for a mean of 4.5 years. The study included 267 hospitals in 19 countries and was conducted between 1994 and 1999. INTERVENTION: Patients were randomly assigned to receive ramipril, up to 10 mg/d (n = 2837), or placebo (n = 2883). MAIN OUTCOME MEASURE: Diagnosis of diabetes determined from self-report at follow-up visits every 6 months, compared between the 2 groups. RESULTS: One hundred and two individuals (3.6%) in the ramipril group developed diabetes compared with 155 (5.4%) in the placebo group (relative risk [RR], 0.66; 95% confidence interval [CI], 0.51-0.85, P<.001). Similar results were noted when different diagnostic criteria were used; in the ramipril group, the RR for diagnosis of diabetes and hemoglobin A(1c) greater than 110% was 0.60 (95% CI, 0.43-0.85), for initiation of glucose-lowering therapy, 0.56 (95% CI, 0.41-0.77), and for both, 0.51 (95% CI, 0.34-0.76). These effects were also consistently seen in several subgroups examined. CONCLUSIONS: Ramipril is associated with lower rates of new diagnosis of diabetes in high-risk individuals. Because these results have important clinical and public health implications, this hypothesis requires prospective confirmation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".