Increased occurrence of diabetes in people with ischemic cardiovascular disease and general and abdominal obesity.
Bibliographic record
Abstract
BACKGROUND: Diabetes worsens the prognosis of patients with ischemic cardiovascular disease (ICVD). Increased body weight and abdominal obesity have been shown to increase the risk of diabetes in people without ICVD. Such a relationship has not been assessed in patients with ICVD who may have a different occurrence due to their disease and medications. OBJECTIVE: To examine the risk of developing diabetes among patients with ICVD according to body mass index (BMI), waist-to-hip ratio and waist circumference METHODS: Anthropometric measurements were done in 4699 men and 1187 women with ICVD (mean age 66 years) and without known diabetes at entry to the Heart Outcomes Prevention Evaluation (HOPE) study. During the median 4.5-year follow-up, a diagnosis of diabetes was reported in 261 (4.4%) participants. RESULTS: There was a positive and graded association between increased BMI, waist circumference and waist-to-hip ratio, and the risk of developing diabetes (P for trends <0.0001). After adjusting for all baseline characteristic differences including medications, the relative risk of developing diabetes after the first 40th percentile of each anthropometric measure increased by 12% (95% CI 9% to 15%) for every 1 kg/m2 increase in BMI; the relative risk increased by 5% (95% CI 4% to 6%) for every 1 cm increase in waist circumference and by 38% (95% CI 18% to 61%) for every 0.1 unit increase in waist-to-hip ratio. CONCLUSION: In patients with ICVD, increased BMI, waist-to-hip ratio and particularly waist circumference constitute independent risk factors for development of 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".