Meta-Analysis of the Effect of Diabetes on Restenosis Rates Among Patients Receiving Coronary Angioplasty Stenting
Bibliographic record
Abstract
OBJECTIVE: Coronary artery disease is a significant source of morbidity and mortality in patients with diabetes. Angioplasty has been associated with higher rates of restenosis in patients with diabetes. Numerous studies have been performed to determine whether coronary artery stenting would be useful in decreasing rates of restenosis of coronary vessels in patients with diabetes. This meta-analysis was conducted to determine the effect of diabetes on angiographic restenosis in patients undergoing coronary stenting. RESEARCH DESIGN AND METHODS: Six studies were included comprising 6,236 individuals-1,166 with diabetes and 5,070 without. The relationships between restenosis rates and age, the percentage of the study population that was male, and the percentage of the study population receiving insulin therapy were examined. RESULTS: The average restenosis rates among patients with and without diabetes were 36.7 and 25.9%, respectively. Restenosis rates were higher among older populations and populations in which a greater percentage of patients with diabetes were treated with insulin, but they did not vary according to the percentage of men in the studies. The odds ratio of coronary artery restenosis associated with diabetes was 1.61 (95% CI 1.21-2.14, P = 0.004) in univariate logistic regression models, but decreased to 1.30 (0.99-1.70, P = 0.055) after controlling for age in multivariate models, suggesting that the higher restenosis rates found in patients with diabetes can in good part be explained by the older ages of patients with diabetes in these studies. CONCLUSIONS: Although diabetes is a risk factor for restenosis after coronary angioplasty stenting, the apparent effect of diabetes on restenosis rates in the published literature is overstated and was reduced in this meta-analysis by approximately half after adjusting for differences in age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".