The Effect of Intensive Glycemic Treatment on Coronary Artery Calcification in Type 1 Diabetic Participants of the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study
Bibliographic record
Abstract
The Epidemiology of Diabetes Interventions and Complications (EDIC) study, an observational follow-up of the Diabetes Control and Complications Trial (DCCT) type 1 diabetes cohort, measured coronary artery calcification (CAC), an index of atherosclerosis, with computed tomography (CT) in 1,205 EDIC patients at approximately 7-9 years after the end of the DCCT. We examined the influence of the 6.5 years of prior conventional versus intensive diabetes treatment during the DCCT, as well as the effects of cardiovascular disease risk factors, on CAC. The prevalences of CAC >0 and >200 Agatston units were 31.0 and 8.5%, respectively. Compared with the conventional treatment group, the intensive group had significantly lower geometric mean CAC scores and a lower prevalence of CAC >0 in the primary retinopathy prevention cohort, but not in the secondary intervention cohort, and a lower prevalence of CAC >200 in the combined cohorts. Waist-to-hip ratio, smoking, hypertension, and hypercholesterolemia, before or at the time of CT, were significantly associated with CAC in univariate and multivariate analyses. CAC was associated with mean HbA(1c) (A1C) levels before enrollment, during the DCCT, and during the EDIC study. Prior intensive diabetes treatment during the DCCT was associated with less atherosclerosis, largely because of reduced levels of A1C during the DCCT.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".